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In spatial augmented reality, a widely dynamic projection mapping system has been developed as a novel approach to graphics presentation for widely moving objects in dynamic situations. However, this method necessitates a novel tracking marker design that is resistant to random and complex occlusion and out-of-focus blurring, which conventional markers have not achieved. This article presents a uniform circumferential marker that becomes an ellipse in perspective projection and expresses geometric information. It can track the relative posture of a dynamically moving sphere with high speed, high accuracy, and robustness owing to continuous contour lines, thereby supporting both wide-range movement in the depth direction and human interaction. Moreover, a biased circumferential marker is proposed to embed unique coding, where the absolute posture is decoded with a novel recognition algorithm. Moreover, rough initialization using the geometry of multiple ellipses is proposed for both markers to start the automatic and precise tracking. Real-time rotation visualization onto the surface of a moving sphere is made possible with the high-speed, widely dynamic projection mapping system. The tracking performance is demonstrated to exhibit sufficient basic tracking performance as well as robustness against blurring and occlusion compared to conventional dot-based markers. [ABSTRACT FROM AUTHOR]

This communication presents a differential retrodirective array (RDA) for real-time tracking. The proposed differential RDA consists of three phase-conjugating chips and two circularly polarized antenna arrays. The phase-conjugating circuit chip, which is in $0.18~\mu \text{m}$ CMOS process, includes a differential low-noise amplifier, a double-balanced mixer, and a differential power amplifier. The specifically designed chip consumes 38.4 mW with a low voltage supply of 1.2 V. Experimental results of monostatic and bistatic cases show that the RDA has a 3 dB beamwidth up to 77°. To the best knowledge of the authors, the proposed RDA with circular polarization, which is promising for tracking free-moving targets, is the first differential RDA enabled by specifically designed low-cost CMOS chips. [ABSTRACT FROM AUTHOR]

In this article, we present a system that combines indoor positioning with a compression algorithm for trajectories in the context of a nursing home. Our aim is to gather and effectively represent the location of the residents and caregivers along time, while allowing for efficient access to those data. We briefly show the system architecture that enables the automatic tracking of user's movements and consequently the gathering of their locations. Then, we present indRep, our compact representation to handle positioning data using grammar-based compression, and provide two basic operations that enable pseudorandom access to the data. Finally, we include experiments that show that indRep is competitive with well-known general-purpose compressors in terms of compression effectiveness and also provides fast access to the compressed data. We expect both features would enable exploitation functionalities even in computers with rather low computational resources. [ABSTRACT FROM AUTHOR]

This article develops recursive algorithms to track shifts in the resonance frequency of linear systems in real time. To date, automatic resonance tracking has been limited to nonmodel-based approaches, which relies solely on the phase difference between a specific input and output of the system. Instead, we propose a transformation of the system into a complex-valued representation, which allows us to abstract the resonance shifts as an exogenous disturbance acting on the excitation frequency, perturbing the excitation frequency from the natural frequency of the plant. We then discuss the resonance-tracking task in two parts: recursively identifying the frequency disturbance and incorporating an update of the excitation frequency in the algorithm. The complex representation of the system simplifies the design of resonance-tracking algorithms due to the applicability of well-established techniques. We discuss the stability of the proposed scheme, even in cases that seriously challenge current phase-based approaches, such as nonmonotonic phase differences and multiple-input multiple-output systems. Numerical simulations further demonstrate the performance of the proposed resonance-tracking scheme. [ABSTRACT FROM AUTHOR]

Saliency detection is an effective front-end process to many security-related tasks, e.g. automatic drive and tracking. Adversarial attack serves as an efficient surrogate to evaluate the robustness of deep saliency models before they are deployed in real world. However, most of current adversarial attacks exploit the gradients spanning the entire image space to craft adversarial examples, ignoring the fact that natural images are high-dimensional and spatially over-redundant, thus causing expensive attack cost and poor perceptibility. To circumvent these issues, this paper builds an efficient bridge between the accessible partially-white-box source models and the unknown black-box target models. The proposed method includes two steps: 1) We design a new partially-white-box attack, which defines the cost function in the compact hidden space to punish a fraction of feature activations corresponding to the salient regions, instead of punishing every pixel spanning the entire dense output space. This partially-white-box attack reduces the redundancy of the adversarial perturbation. 2) We exploit the non-redundant perturbations from some source models as the prior cues, and use an iterative zeroth-order optimizer to compute the directional derivatives along the non-redundant prior directions, in order to estimate the actual gradient of the black-box target model. The non-redundant priors boost the update of some “critical” pixels locating at non-zero coordinates of the prior cues, while keeping other redundant pixels locating at the zero coordinates unaffected. Our method achieves the best tradeoff between attack ability and perturbation redundancy. Finally, we conduct a comprehensive experiment to test the robustness of 18 state-of-the-art deep saliency models against 16 malicious attacks, under both of white-box and black-box settings, which contributes a new robustness benchmark to the saliency community for the first time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel reweighted price relative tracking (RPRT) system for automatic portfolio optimization (APO). In the price prediction stage, it automatically assigns separate weights to the price relative predictions according to each asset’s performance, and these weights will also be automatically updated. In the portfolio optimizing stage, a novel tracking system with a generalized increasing factor is proposed to maximize the future wealth of next period. Besides, an efficient algorithm is designed to solve the portfolio optimization objective, which is applicable to large-scale and time-limited situations. Extensive experiments on six benchmark datasets from real financial markets with diverse assets and different time spans are conducted. RPRT outperforms other state-of-the-art systems in cumulative wealth, mean excess return, annual percentage yield, and some typical risk metrics. Moreover, it can withstand considerable transaction costs and runs fast. It indicates that RPRT is an effective and efficient APO system. [ABSTRACT FROM AUTHOR]

In this article, a microcontroller-based high-accuracy dew-point (DP) instrument using a quartz crystal resonance (QCR) sensor is developed. The sensor is a symmetrical double cooled QCR DP sensor, which has the characteristics of efficient cooling and stable oscillation. Based on the proportional—integral—differential control method, the logic level signal and the oscillation amplitude outputted by the drive circuit are used as control feedback to obtain an optimized expert strategy control system, and the automatic DP tracking measurement is realized by the microcontroller. It is proved by experiments that the measurement error of the instrument in the range of 0–15 °C dew point (DP) environment is less than 0.3 °C DP, and it has a good performance of tracking measurement in a dynamic DP environment. [ABSTRACT FROM AUTHOR]

Automatic tracking of particles in time-lapse fluorescence microscopy images is essential for quantifying the dynamic behavior of subcellular structures and virus structures. We introduce a novel particle tracking approach based on a deep recurrent neural network architecture that exploits past and future information in both forward and backward direction. Assignment probabilities are determined jointly across multiple detections, and the probability of missing detections is computed. In addition, existence probabilities are determined by the network to handle track initiation and termination. For correspondence finding, track hypotheses are propagated to future time points so that information at later time points can be used to resolve ambiguities. A handcrafted similarity measure and handcrafted motion features are not necessary. Manually labeled data is not required for network training. We evaluated the performance of our approach using image data of the Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods. [ABSTRACT FROM AUTHOR]

The analysis of the dynamic behavior of cells in time-lapse microscopy sequences requires the development of reliable and automatic tracking methods capable of estimating individual cell states and delineating the lineage trees corresponding to the tracks. In this paper, we propose a novel approach, i.e., an ant colony inspired multi-Bernoulli filter, to handle the tracking of a collection of cells within which mitosis, morphological change and erratic dynamics occur. The proposed technique treats each ant colony as an independent one in an ant society, and the existence probability of an ant colony and its density distribution approximation are derived from the individual pheromone field and the corresponding heuristic information for the approximation to the multi-Bernoulli parameters. To effectively guide ant foraging between consecutive frames, a dual prediction mechanism is proposed for the ant colony and its pheromone field. The algorithm performance is tested on challenging datasets with varying population density, frequent cell mitosis and uneven motion over time, demonstrating that the algorithm outperforms recently reported approaches. [ABSTRACT FROM AUTHOR]

This study investigates the information content of ship noise received on a drifting volumetric array of hydrophones in shallow water marine environments for the purposes of conducting acoustic thermometry or other environmental inversions. Passive inversions for physical oceanographic parameters are conducted using travel time differences, determined by cross-correlating ship noise received on hydrophones suspended beneath drifting buoys. Ships are tracked using the automatic identification system. Information content gained from the inversion is assessed using traditional a posteriori error analysis. Numerical simulations using a standard normal mode propagation model are used to test limitations of the proposed approach with respect to frequency band, drifting receiver configuration, precision, and accuracy of the inversion results, along with sensitivity to environmental and position mismatch. Performance predictions using this model are compared with results from a field experiment using at-sea data collected off the coast of New London, CT, USA, in Long Island Sound. Information gathered using passive acoustic inversion methods on drifting arrays can be used to constrain general circulation models, in coastal environments, where ship noise is ubiquitous, environmental data is sparse, and the oceanography is dynamic and important for understanding large-scale ocean processes. [ABSTRACT FROM AUTHOR]

A novel control scheme is proposed in this paper for the sensorless control of interior permanent magnet synchronous machines (IPMSMs) with the automatic tracking of the maximum-torque-per-ampere (MTPA) trajectory. Differing from the conventional method that the estimation of the rotor position and the tracking of MTPA trajectory are two separate processes, the proposed control scheme can achieve sensorless MTPA control of IPMSMs in one step by defining and constructing a proper virtual q-axis inductance to directly calculate the current space vector angle along the MTPA trajectory rather than the rotor position, which greatly simplifies the whole implementation process. Meanwhile, considering that the virtual q-axis inductance is changing with the machine parameters and the applied load, a simple signal processing technique based on the virtual high-frequency signal injection is developed for the real time and accurate acquisition of the virtual q-axis inductance. Furthermore, in order to ensure the reliability and stability of the proposed control scheme, the effective range of the virtual q-axis inductance is analyzed in detail. Finally, the experimental results on an IPMSM demonstrate the proposed control scheme can achieve the high-performance sensorless control with accurate MTPA self-tracking operation. [ABSTRACT FROM AUTHOR]

Quadrature spatial modulation (QSM) using frequency modulated diverse retrospective array (FMD-RDA) is put forward to achieve green transmission paradigm shift from phased-array-based arrangement. Up to date, the QSM techniques have employed phased-array, but only angular focusing direction is possible without range focusing direction. On the other hand, millimeter-wave (mmWave) scheme is suggested as an alternative approach for fifth-generation because of large bandwidth spectrum. Therefore, this paper studies green secure communication range-angle focusing QSM FMD-RDA in mmWave wireless communication. In FMD-RDA, a small frequency increment is included across the RDA elements to decouple range-angle focusing. More importantly, an attractive advantage of using FMD-RDA is the automatic self tracking feature which alleviate transmitter/receiver's channel state information (CSI) requirement. To ensure secure transmission, we exploit physical layer security to degrade the channel quality of the eavesdropper. Furthermore, low complexity greedy detector is devised to retrieved the information bits at the receiver. We also derived pair-wise error probability, and analyze secrecy sum rate and energy transfer capability. The reported simulation results demonstrate that the proposed scheme is effective in improving the spectral efficiency with low complexity and no CSI requirement. [ABSTRACT FROM AUTHOR]

Localization and tracking are basic services for the applications of autonomous underwater vehicles (AUVs). Although they are closely related, the localization or tracking is usually studied independently. This paper addresses a joint localization and tracking issue for an AUV, subject to asynchronous clocks in physical channel and state disturbances in control channel. We first develop an asynchronous localization algorithm to estimate the position of an AUV, wherein the clocks on AUV and buoys are not required to be synchronized. With the estimated position, a model-free tracking controller is designed for an AUV to track the reference trajectory. Particularly, the time-delay estimation and dynamic gain strategies are adopted to reduce the influence of state disturbances. Moreover, the convergence conditions and Cramér–Rao lower bounds for the localization algorithm are derived. For the tracking controller, Lyapunov functions are constructed to analyze the stability, through which the sufficient conditions for asymptotic stability are presented. Finally, simulation and experiment results are given to show the effectiveness of the proposed method. It is demonstrated that the asynchronous localization algorithm in this paper can effectively eliminate the impact of the clock skew and offset. Meanwhile, the tracking accuracy can be significantly improved. [ABSTRACT FROM AUTHOR]

The existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic. It is urgent to find an approach to collecting the high-resolution real-time status of unconnected road users. This paper introduces a new-generation light detection and ranging (LiDAR) enhanced connected infrastructures that actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The LiDAR data processing procedure, including background filtering, object clustering, vehicle recognition, lane identification, and vehicle tracking, is presented in this paper. The performance of the proposed data processing procedure is evaluated with the field collected data. [ABSTRACT FROM AUTHOR]

Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-tag localization, in which each object is attached with one tag. In this paper, we propose a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader’s antenna spacing and placement; 2) supporting accurate calibration of the reader’s antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader’s antenna geometry does have impact on tag positioning performance. [ABSTRACT FROM PUBLISHER]

This paper addresses the problem of robust fault-tolerant control of spacecraft attitude stabilization in the presence of model uncertainties, actuator failures, and external disturbances simultaneously. Utilizing the fast nonsingular terminal sliding mode control technique, a novel finite-time extended state observer is first proposed to estimate and compensate for the specified synthetic uncertainties derived from actuator failures and/or model deviations. And also the detailed derivations of the observer are provided, along with a thorough analysis for the associated ultimately bounded stability and estimation error convergence property in the sense of finite-time control. Then, with the reconstructed information achieving from the finite-time observer, an adaptive robust sliding mode based fault-tolerant control approach is developed to ensure that the closed-loop attitude control system reach the real sliding mode surface in finite time. Meanwhile, the chattering problem has been restrained via the modified gain adjusting law. The key feature of the proposed strategies is that the whole closed-loop fault-tolerant control system can be guaranteed theoretically to be finite-time stable by the development of Lyapunov methodology. Finally, numerical simulation results are presented to illustrate and highlight the fine performance benefits obtained using the proposed schemes. [ABSTRACT FROM AUTHOR]

Monitoring sea ice activities is particularly critical to safe naval operations in the Arctic Ocean. Accurately tracking sea ice motions is essential to validate or even improve sea ice models for ice hazard forecasts at a fine scale. Fine-scale motions can be tracked from high-resolution radar or optical satellite imagery but with limited coverage. Daily motions over the entire Arctic are retrievable from passive microwave data, but at a much lower spatial resolution. Thus, providing motions at the passive microwave spatial and temporal coverage, but at an enhanced spatial resolution, will be a significant benefit. To break the resolution limitation and to boost tracking accuracy, a sequential super-resolved fine-scale sea ice motion tracking framework is proposed in which a hybrid example-based single image super-resolution algorithm is employed before the tracking procedure. Experiments demonstrate that the proposed framework significantly improves the tracking performance in both accuracy and robustness for a benchmark algorithm and a recently proposed state-of-the-art tracking algorithm. [ABSTRACT FROM PUBLISHER]

We present a fully automated multi-sperm tracking algorithm. It has the demonstrated capability to detect and track simultaneously hundreds of sperm cells in recorded videos while accurately measuring motility parameters over time and with minimal operator intervention. Algorithms of this kind may help in associating dynamic swimming parameters of human sperm cells with fertility and fertilization rates. Specifically, we offer an image processing method, based on radar tracking algorithms, that detects and tracks automatically the swimming paths of human sperm cells in timelapse microscopy image sequences of the kind that is analyzed by fertility clinics. Adapting the well-known joint probabilistic data association filter (JPDAF), we automatically tracked hundreds of human sperm simultaneously and measured their dynamic swimming parameters over time. Unlike existing CASA instruments, our algorithm has the capability to track sperm swimming in close proximity to each other and during apparent cell-to-cell collisions. Collecting continuously parameters for each sperm tracked without sample dilution (currently impossible using standard CASA systems) provides an opportunity to compare such data with standard fertility rates. The use of our algorithm thus has the potential to free the clinician from having to rely on elaborate motility measurements obtained manually by technicians, speed up semen processing, and provide medical practitioners and researchers with more useful data than are currently available. [ABSTRACT FROM AUTHOR]

This brief presents a sequential fusion estimation method for maneuvering target tracking in asynchronous wireless sensor networks. The modeling error caused by asynchronous sampling and communication uncertainties is considered and compensated for by introducing a time-varying fading factor into the unscented Kalman filter (UKF). A square root form of the unscented strong tracking filter (SR-USTF) based on QR decomposition is proposed to improve the stability and performance of the USTF. Moreover, a hybrid sequential fusion estimation method is presented to estimate the state of the target, and the proposed sequential fusion estimation method combines the superiorities of both the SR-USTF and the conventional UKF, and is able to deal with communication uncertainties such as time delay and packet loss in a unified framework. Both simulations and experiments of an E-puck robot tracking example are provided to demonstrate the effectiveness and superiorities of the proposed sequential fusion estimation method. [ABSTRACT FROM AUTHOR]

We propose a novel model-based method for tracking the 6-DOF pose of a very large number of rigid objects in real time. By combining dense motion and depth cues with sparse keypoint correspondences, and by feeding back information from the modeled scene to the cue extraction process, the method is both highly accurate and robust to noise and occlusions. A tight integration of the graphical and computational capability of graphics processing units allows the method to simultaneously track hundreds of objects in real time. We achieve pose updates at framerates around 40 Hz when using 500 000 data samples to track 150 objects using images of resolution $640\times 480$ . We introduce a synthetic benchmark data set with varying objects, background motion, noise, and occlusions that enables the evaluation of stereo-vision-based pose estimators in complex scenarios. Using this data set and a novel evaluation methodology, we show that the proposed method greatly outperforms state-of-the-art methods. Finally, we demonstrate excellent performance on challenging real-world sequences involving multiple objects being manipulated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a deep metric learning (DML) approach for robust visual tracking under the particle filter framework. Unlike most existing appearance-based visual trackers, which use hand-crafted similarity metrics, our DML tracker learns a nonlinear distance metric to classify the target object and background regions using a feed-forward neural network architecture. Since there are usually large variations in visual objects caused by varying deformations, illuminations, occlusions, motions, rotations, scales, and cluttered backgrounds, conventional linear similarity metrics cannot work well in such scenarios. To address this, our proposed DML tracker first learns a set of hierarchical nonlinear transformations in the feed-forward neural network to project both the template and particles into the same feature space where the intra-class variations of positive training pairs are minimized and the interclass variations of negative training pairs are maximized simultaneously. Then, the candidate that is most similar to the template in the learned deep network is identified as the true target. Experiments on the benchmark data set including 51 challenging videos show that our DML tracker achieves a very competitive performance with the state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of single-target tracker performance evaluation. We consider the performance measures, the dataset and the evaluation system to be the most important components of tracker evaluation and propose requirements for each of them. The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison. The ranking-based methodology addresses tracker equivalence in terms of statistical significance and practical differences. A fully-annotated dataset with per-frame annotations with several visual attributes is introduced. The diversity of its visual properties is maximized in a novel way by clustering a large number of videos according to their visual attributes. This makes it the most sophistically constructed and annotated dataset to date. A multi-platform evaluation system allowing easy integration of third-party trackers is presented as well. The proposed evaluation methodology was tested on the VOT2014 challenge on the new dataset and 38 trackers, making it the largest benchmark to date. Most of the tested trackers are indeed state-of-the-art since they outperform the standard baselines, resulting in a highly-challenging benchmark. An exhaustive analysis of the dataset from the perspective of tracking difficulty is carried out. To facilitate tracker comparison a new performance visualization technique is proposed. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT) but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement, and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations c comparing the mean optimal subpattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: $N$-scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good tradeoff between computational complexity and the MOSPA performance. [ABSTRACT FROM PUBLISHER]

Even though many public parking lots are located underground and indoors, most existing automatic parking space detection and tracking methods have difficulty handling such scenarios due to severe illumination and complex obstacle conditions. To overcome this problem, this paper proposes a method that detects and tracks parking spaces in underground and indoor environments by fusing sensors already mounted on mass produced vehicles. The proposed detection method finds parking spaces based on a high-level fusion of two complementary approaches: parking slot marking-based and free space-based. Parking slots are detected by estimating parallel line pairs and free spaces are detected by recognizing the positions of parked vehicles as well as pillars. The proposed tracking method enhances the previous method by considering pillar information. Since pillars degrade parking slot tracking performance, this method estimates a pillar region and utilizes it to remove false edges and to estimate the amount of occlusion. [ABSTRACT FROM PUBLISHER]

This paper presents a general methodology of inductive power delivery in wireless chronic rodent electrophysiology applications. The focus is on such systems design considerations under the following key constraints: maximum power delivery under the allowable specific absorption rate (SAR), low cost and spatial scalability. The methodology includes inductive coil design considerations within a low-frequency ferrite-core-free power transfer link which includes a scalable coil-array power transmitter floor and a single-coil implanted or worn power receiver. A specific design example is presented that includes the concept of low-SAR cellular single-transmitter-coil powering through dynamic tracking of a magnet-less receiver spatial location. The transmitter coil instantaneous supply current is monitored using a small number of low-cost electronic components. A drop in its value indicates the proximity of the receiver due to the reflected impedance of the latter. Only the transmitter coil nearest to the receiver is activated. Operating at the low frequency of 1.5 MHz, the inductive powering floor delivers a maximum of 15.9 W below the IEEE C95 SAR limit, which is over three times greater than that in other recently reported designs. The power transfer efficiency of 39% and 13% at the nominal and maximum distances of 8 cm and 11 cm, respectively, is maintained. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification. [ABSTRACT FROM PUBLISHER]

A robust algorithm is proposed for tracking a target object in dynamic conditions including motion blurs, illumination changes, pose variations, and occlusions. To cope with these challenging factors, multiple trackers based on different feature representations are integrated within a probabilistic framework. Each view of the proposed multiview (multi-channel) feature learning algorithm is concerned with one particular feature representation of a target object from which a tracker is developed with different levels of reliability. With the multiple trackers, the proposed algorithm exploits tracker interaction and selection for robust tracking performance. In the tracker interaction, a transition probability matrix is used to estimate dependencies between trackers. Multiple trackers communicate with each other by sharing information of sample distributions. The tracker selection process determines the most reliable tracker with the highest probability. To account for object appearance changes, the transition probability matrix and tracker probability are updated in a recursive Bayesian framework by reflecting the tracker reliability measured by a robust tracker likelihood function that learns to account for both transient and stable appearance changes. Experimental results on benchmark datasets demonstrate that the proposed interacting multiview algorithm performs robustly and favorably against state-of-the-art methods in terms of several quantitative metrics. [ABSTRACT FROM PUBLISHER]

This paper presents a tracking control method for the lateral motion of an autonomous land vehicle (ALV). This method is based on active disturbance rejection control (ADRC) scheme and differential flatness theory. The lateral motion is hard to control since it is underactuated, nonlinear, and with large uncertainties. By making a small-angle approximation, the dynamic model is linearized. The flatness of the linear model is proved and a flat output is found. An equivalent form of the model is obtained based on the flat output and its derivatives only. Moreover, an ADRC is adopted to guarantee both control accuracy and strong robustness. Simulation results are presented and the results show the effectiveness of the control strategies. [ABSTRACT FROM PUBLISHER]

This paper presents a constructive method to design distributed controllers that force a group of $N$ underactuated ships under sea loads to perform a desired synchronization motion tracking task and guarantee no collisions between the ships. First, an observer is designed to globally exponentially estimate the sea loads. Then, the ship dynamics is transformed to an almost spherical form. Smooth or $p$-times differentiable step functions are next introduced to ensure that the collision avoidance is only active when the ships are in their collision detection zones. For each ship, a dynamical variable is introduced to deal with relaxation of the reference path generation and difficulties caused by underactuation. The control design is based on the above developments, and Lyapunov and backstepping methods. The effectiveness of the proposed results is illustrated through simulations. [ABSTRACT FROM PUBLISHER]

Sparse representation (SR)-based tracking systems have become popular in the past recent years for its effectiveness. However, the underlying assumption of these tracking systems is that the target appearance can be linearly represented by a sparse approximation over a set of templates and a residual term, which usually needs to solve $\ell_1$ norm minimization for many times and brings a heavy computational cost. This paper introduces an efficient tracking system by discovering orthogonalized templates. By orthogonalizing templates from previous frames and removing their correlation, we show that the sparsity of template weights is not necessary in target appearance modeling and thus a least squares regularization can be employed. We also decompose the residual term into two components in observation model to take occlusion cases into consideration. We demonstrate that, in comparison with the SR-based tracking systems that use $\ell_1$ learning, our tracking system is much more computationally efficient while getting an even better performance. Experiments on a variety of challenging video sequences demonstrate both the effectiveness and efficiency of the system. [ABSTRACT FROM PUBLISHER]

A heuristic track-to-track association algorithm for sensors with azimuth biases and range biases is proposed based on a reference pattern (REP) of the target. For a given target, the structure revealed by its neighboring targets is named as its REP. To compensate for the relative azimuth bias between sensors, we modify the optimal subpattern assignment metric, which makes the derived association cost matrix insensitive to sensor biases. Simulation results demonstrate the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The deterministic Laplace method is combined with particle filtering for the state estimation in nonlinear state-space models. Multidimensional Laplace approximations of the posterior moments are embedded in a particle filter. These approximations are used to design an affine transformation taking into account the observation. This transformation is applied to the particle sample in order to improve particle approximation. The resulting algorithm is called the Laplace particle filter (LPF). It is tested by simulation and exhibits good performance on three tracking problems: bearings-only target tracking, ballistic target tracking during atmospheric reentry, and neural decoding for motion analysis. [ABSTRACT FROM AUTHOR]

The linear minimum mean-square error (LMMSE) estimation plays an important role in nonlinear estimation. It is the best of all estimators that are linear in the measurement. However, it may not perform well for a highly nonlinear problem. A generalized linear estimation framework, namely, linear in transform (LIT) estimation, is proposed in this work. It employs a measurement transform function (MTF) and finds the best estimator among all estimators that are linear in MTF, rather than in the measurement itself. The performance of LIT estimation with a proper MTF can be superior to LMMSE estimation due to the benefit of a more appropriate or larger candidate set of estimators determined by the MTF (compared to the set of all linear estimators in LMMSE estimation). Since systematic procedures for constructing an MTF that guarantees enhanced performance for the general case are difficult to produce, we provide several design guidelines, which are illustrated by a numerical example. Further, similar to LMMSE estimation, moments involved in LIT estimation are difficult to compute analytically in general. Fortunately, many numerical approximations for LMMSE estimation are also applicable to LIT estimation. Approximation of LIT estimation based on the Gauss-Hermite quadrature is presented. Our LIT estimation is demonstrated by applications to target tracking. Its performance is compared with LMMSE estimation by Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Estimation for discrete-time stochastic systems with parameters varying in a continuous space is considered in this paper. Justified by an analysis of model approximation, a novel approach, called hybrid grid multiple model (HGMM), is proposed for state estimation. The model set used by HGMM is a combination of a fixed coarse grid and an adaptive fine grid to cover the mode space with a relatively small number of models. Next, two fundamental problems of the HGMM approach?model-set sequence-conditioned estimation and design of adaptive fine models?are addressed. Then, based on two model-set designs by moment matching, HGMM estimation algorithms are presented. Finally, performance of the developed HGMM estimation algorithms is evaluated on benchmark tracking scenarios, and simulation results demonstrate their superiority to the state-of-the-art MM estimation algorithms in terms of accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a new collaborative tracking algorithm is put forward to track multiple objects in video streams. First, we suggest a robust color-based tracker whose model is updated by online learned contextual information. A recursive method is performed to improve the estimation accuracy and the robustness to cluttered environment. Then, we extend this tracker to multiple targets. To avoid the problem of id-switch in long-term occlusion, we design a hierarchical tracking system with different tracking priorities. First, the algorithm employs an adaptive collision prevention model to separate the nearby trajectories. When the inter-occlusion happens, the holistic model of tracker splits into several parts, and we use the visible parts to perform tracking as well as occlusion reasoning. In the case where the targets have close appearance models, a trajectory monitoring approach is employed to handle the occlusion. Once the tracker is fully occluded, the algorithm will reinitialize particles around the occluder to capture the reappeared target. Experimental results using open dataset demonstrate the feasibility of our proposal. In addition, comparison with several state-of-the-art trackers has also been performed. [ABSTRACT FROM PUBLISHER]

In the field of target tracking, a tremendous amount of work has been done on designing and implementing algorithms. However, much less work has been performed on analyzing whether, for a given target in a given environment, tracking is possible at all. Our recent work developed a framework to answer just that. But the only clutter amplitude model it could process was Rayleigh. On the other hand, the K-distribution, much heavier-tailed than Rayleigh, has recently been posited to more accurately describe “real” sonar clutter. In this paper, we incorporate K-distributed clutter into the trackability framework, and the differences are significant. We find we answer the question, “When tracking a target, does using amplitude actually help?” [ABSTRACT FROM AUTHOR]

Locating and evaluating the length and severity of a stenosis is very important for planning adequate treatment of peripheral arterial disease (PAD). Conventional ultrasound (US) examination cannot provide maps of entire lower limb arteries in 3-D. We propose a prototype 3D-US robotic system with B-mode images, which is nonionizing, noninvasive, and is able to track and reconstruct a continuous segment of the lower limb arterial tree between the groin and the knee. From an initialized cross-sectional view of the vessel, automatic tracking was conducted followed by 3D-US reconstructions evaluated using Hausdorff distance, cross-sectional area, and stenosis severity in comparison with 3-D reconstructions with computed tomography angiography (CTA). A mean Hausdorff distance of 0.97 \pm 0.46\;\textmm was found in vitro for 3D-US compared with 3D-CTA vessel representations. To evaluate the stenosis severity in vitro, 3D-US reconstructions gave errors of 3%–6% when compared with designed dimensions of the phantom, which are comparable to 3D-CTA reconstructions, with 4%–13% errors. The in vivo system’s feasibility to reconstruct a normal femoral artery segment of a volunteer was also investigated. These results encourage further ergonomic developments to increase the robot’s capacity to represent lower limb vessels in the clinical context. [ABSTRACT FROM AUTHOR]

A four-wheel steering four-wheel drive electric vehicle has a steering motor and a driving motor for each wheel, for a total of eight motors. Almost all of the existing path-tracking controllers for this vehicle are designed by viewing the mathematical model of the vehicle as the system of equations \dot x = f(x,u), y = h(x,u) , and are mathematically sophisticated. In contrast, this brief presents a block diagrammatic representation of the model, and exploits the natural feedback loops revealed through this representation to develop two novel and useful results: 1) a mathematically simpler path-tracking controller that promises to be easier to tune and 2) a constraint on the wheel accelerations that helps constrain the wheel slips to a desired value. Simulations illustrate this solution. [ABSTRACT FROM PUBLISHER]

In this paper, new methodologies for the estimation of the depth of a target with unknown dimensions, based on depth from focus strategies, are proposed. The measurements are extracted from images acquired with a single camera, resorting to the minimization of a new functional, deeply rooted on the optical characteristics of the lens system. The analysis and synthesis of two complementary filters and a linear parametrically varying observer are discussed in detail. These estimators use information present on the boundary of the target, which is assumed to be on a plane parallel to the camera sensor, and whose dimensions are considered to remain constant over time. This paper complements a single pan and tilt camera-based indoor positioning and tracking system. To assess the performance of the proposed solutions, a series of indoor experimental tests for a range of operation of up to ten meters, which included tracking and localizing a small unmanned aerial vehicle with unknown dimensions, was carried out. Depth estimates with accuracies on the order of a few centimeters were obtained. [ABSTRACT FROM PUBLISHER]

In this paper, a unified control framework is proposed to realize a robotic ball catching task with only a moving single-camera (eye-in-hand) system able to catch flying, rolling, and bouncing balls in the same formalism. The thrown ball is visually tracked through a circle detection algorithm. Once the ball is recognized, the camera is forced to follow a baseline in the space so as to acquire an initial dataset of visual measurements. A first estimate of the catching point is initially provided through a linear algorithm. Then, additional visual measurements are acquired to constantly refine the current estimate by exploiting a nonlinear optimization algorithm and a more accurate ballistic model. A classic partitioned visual servoing approach is employed to control the translational and rotational components of the camera differently. Experimental results performed on an industrial robotic system prove the effectiveness of the presented solution. A motion-capture system is employed to validate the proposed estimation process via ground truth. [ABSTRACT FROM PUBLISHER]

A wirelessly powered homecage system, called the EnerCage-HC, that is equipped with multicoil wireless power transfer, closed-loop power control, optical behavioral tracking, and a graphic user interface is presented for longitudinal electrophysiology and behavioral neuroscience experiments. The EnerCage-HC system can wirelessly power a mobile unit attached to a small animal subject and also track its behavior in real-time as it is housed inside a standard homecage. The EnerCage-HC system is equipped with one central and four overlapping slanted wire-wound coils with optimal geometries to form three- and four-coil power transmission links while operating at 13.56 MHz. Utilizing multicoil links increases the power transfer efficiency (PTE) compared with conventional two-coil links and also reduces the number of power amplifiers to only one, which significantly reduces the system complexity, cost, and heat dissipation. A Microsoft Kinect installed 90 cm above the homecage localizes the animal position and orientation with 1.6-cm accuracy. Moreover, a power management ASIC, including a high efficiency active rectifier and automatic coil resonance tuning, was fabricated in a 0.35- \mu \textm 4M2P standard CMOS process for the mobile unit. The EnerCage-HC achieves a max/min PTE of 36.3%/16.1% at the nominal height of 7 cm. In vivo experiments were conducted on freely behaving rats by continuously delivering 24 mW to the mobile unit for >7 h inside a standard homecage. [ABSTRACT FROM PUBLISHER]

Joint integrated probabilistic data association (JIPDA) is effective in automatic multitarget tracking in an environment of false and missing measurements. However, JIPDA suffers from coalescence of closely spaced tracks. This paper develops a coalescence-avoiding version of JIPDA, which is named JIPDA. To accomplish this, the descriptor system approach is used that has previously been used for the development of JPDA, i.e., a coalescence-avoiding JPDA version. The effectiveness of JIPDA is demonstrated through simulations. [ABSTRACT FROM AUTHOR]

Wide voltage range operation for DSPs brings more versatility to achieve high energy efficiency in mobile applications. This paper describes a 32 bits DSP fabricated in 28 nm Ultra Thin Body and Box FDSOI technology. Body Biasing Voltage (VBB) scaling from 0 V up to ±2 V decreases the core VDDMIN to 397 mV and increases clock frequency by +400%@500 mV and +114%@1.3 V. The DSP frequency measurements show 2.6 GHz@1.3 V(VDD)@2 V(VBB) and 460 MHz@397 mV(VDD)@2 V(VBB). The lowest peak energy efficiency is measured at 62 pJ/op at 0.53 V. In addition to technological gains, maximum frequency tracking design techniques are proposed for wide voltage range operation. On silicon, at 0.6 V, those techniques allow high energy gain of 40.6% w.r.t. a worst case corner approach. [ABSTRACT FROM PUBLISHER]

Background cues mainly play a supplementary or accompanying role in most previous approaches for object tracking. If object tracking is treated as a binary target/background classification problem, then the similarity with the target and the difference from the background can be considered equally informative. This leads to an interesting question: is it possible to perform object tracking using background cues only? To answer this question, we propose an object tracking approach that utilizes background cues only. The extensive experimental results positively validate the possibility of performing object tracking using background cues only. The results revealed in this paper also provide a new reference in designing future object tracking methods. [ABSTRACT FROM PUBLISHER]