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Vehicle re-identification has the objective of finding a specific vehicle among different vehicle crops captured by multiple cameras placed at multiple intersections. Among the different difficulties, high intra-class variability and high inter-class similarity can be highlighted. Moreover, the resolution of the images can be different, which also means a challenge in the re-identification task. Intending to face these problems, we use as baseline our previous work based on obtaining different deep learning features and ensembling them to get a single, stable and robust feature vector. It also includes post-processing techniques that explode all the information provided by the CityFlowV2-ReID dataset, including a re-ranking step. Then, in this paper, several newly included improvements are described. Background and orientation similarity matrices are added to the system to reduce bias towards these characteristics. Furthermore, we take into account the camera labels to penalize the gallery images that share camera with the query image. Additionally, to improve the training step, a synthetic dataset is added to the original one.

When a large generation-consumption unbalance occurs, under-frequency load shedding (UFLS) is the last resort to restore this balance by disconnecting loads from the electrical power system. In France, the UFLS is realized at the level of primary substations by allocating groups of MV feeders to under-frequency relays. Choosing and implementing this allocation requires substantial operator work, and is currently done once a year in France. In the near future, however, it may become both necessary and technically feasible to update the UFLS scheme more frequently; this is the investigated topic of this paper. Firstly, several re-allocation strategies are defined, whose periodicity ranges from months to hours. Then their applications are simulated on a large real-world dataset based on the actual consumption of individual MV feeders from an entire French region over two years. The performance of the various proposed strategies are compared and recommendations regarding the implementation of future UFLS re-allocation methods are provided.

This paper presents a multicamera system for vehicles detection and their corresponding mapping into the parking spots of a parking lot. Approaches from the state-of-the-art, which work properly in controlled scenarios, have been validated using small amount of sequences and without more challenging realistic conditions (illumniation changes, different weather). On the other hand, most of them are not complete systems, but provide only parts of them, usually detectors. The proposed system has been designed for realistic scenarios considering different cases of occlussion, ilumination changes and different climatic conditions; a real scenario (the International Pittsburgh Airport parking lot) has been targeted with the condition that existing parking security cameras can be used, avoiding the deployment of new cameras or other sensors infrastructures. For design and validation, a new multicamera dataset has been recorded. The system is based on existing object detectors (the results of two of them are shown) and different proposed postprocessing stages. The results clearly show that the proposed system works correctly in challenging scenarios including almost total occlusions, illumination changes and different weather conditions, This work has been partially supported by the Spanish Government FPU grant programme (Ministerio de Educación, Cultura y Deporte) and by the Spanish government under the project TEC2014-53176-R (HAVideo)

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1). Funding Agencies|Slovenian research agency research programs [P2-0214, P2-0094]; Slovenian research agency project [J2-8175]; Czech Science Foundation Project [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme Visual Surveillance and Insight; Faculty of Computer Science, University of Ljubljana, Slovenia

There is a large demand in the area of video-surveillance, especially in people detection, which has caused a large increase in the number of researches and resources in this field. As training images and annotations are not always available, it is important to consider the cost involved in creating the detector models. For example, for elderly people detection, the detector must have into account different positions such as standing, sitting, in a wheelchair, etc. Therefore, this work has the main objective of reducing the amount of resources needed to generate the detection model, saving the cost of having to record new sequences and generate the associated annotations for a detector training. To achieve this, three synthetic image datasets have been created in order to train three different models, evaluating which model is optimal and finally analyzing its feasibility by comparing it with a people detector for wheelchair users trained with real images. Other people detection scenarios in which this technique could be applied are, for example, people riding horses or motorbikes, or people carrying supermarket carts. The synthetic datasets have been generated by combining images of standing people with wheelchair images, combining image patches, and segmenting sections of people (trunk, legs, etc.) to add them to the wheelchair image. As expected, the obtained results have a reduction of efficiency (between 21 and 25%) in exchange for the enormous saving in human annotation and resources to record real images.

Applying people detectors to unseen data is challenging since patterns distributions may significantly differ from the ones of the training dataset. In this paper, we propose a framework to adapt people detectors during runtime classification. Such adaptation takes advantage of multiple detectors to identify their best configurations (i.e. detection thresholds) without requiring manually labeled ground truth. We maximize the mutual information of detectors by pair-wise correlating their outputs to obtain a set of hypotheses for the detection thresholds. These hypotheses are later combined by weighted voting to obtain a final decision for the detection threshold of each detector. The proposed approach does not require re-training detectors and uses standard people detector outputs, i.e., bounding boxes, therefore it can employ various types of detectors. The experimental results demonstrate that the proposed approach outperforms state-of-the-art detectors whose optimal configuration is learned from training data.

In this paper the performance of ITS-G5A communications for an autonomous driving application is analyzed in a real high-density scenario. The data was collected during the cooperative platooning tests that took place in Helmond in the frame of the Grand Cooperative Driving Challenge 2016. In the competition, between 8–10 autonomous vehicles formed two platoons in different lanes and were required to merge into a predefined competition zone. The performance is characterized using CAM CCDFs which serves as a base for the evaluation of a Cooperative Adaptive Cruise Control application. Two important effects has been identified that affect to the reliability of the communications. Firstly, there is a degradation with the distance that appears to be stronger for cars and more gentle for trucks. This indicates that occlusions heavily affect the connectivity of ITS-G5A. Secondly, the reliability is below expectations and some of the vehicles perform consistently worse than others. Although further investigation is required, a possible explanation for this is that a highly congested channel is making some of the vehicles get stuck and are not able to regularly access the channel.

During the European project Local4Global a control and optimization algorithm aimed to systems of systems was designed. Along with the algorithm, a gateway-like software was developed in order to host it. This highly adaptable and extensible framework enables access and control to different device and entities that compose the system it is deployed on. Use of standards and predefined interfaces ensure the interoperability with external entities, physical devices and even other gateway instances. This paper explains how this generic software can be used to build custom-made controllers, designed to be easy to configure, adaptable and scalable.

This paper describes the design and integration of miniature wired robots into a modular and intelligent operation room focused on performing single-incision laparoscopic surgery. These devices were designed to be moved by a robotic arm. Its position was tracked by a tracking camera which consisted of camera and light miniature robots, having the former a stepper motor in order to provide a tilt turn. Some experiments were conducted in order to test the integration of these devices into the system.

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link -- ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

In this paper we study the effectiveness of different algorithms for texture classification based on the Local Binary Patterns method, in order to obtain in future a tracking system for surgical gauzes during a laparoscopic operation. Due to the mobility of the camera and the lack of a precise control over its position, the algorithms must work under unknown illumination and viewpoint parameters. Our intention is to provide to surgeons a simple tracking system in order to avoid mislaid gauzes during the operations, which might be completely unattended so that the surgical team can concentrate on the patient and not on the gauze counting.

Summary form only given. A Heavy Neutral Beam Probe (HNBP) has been developed for measuring the space potential of the Helimak1 low temperature (Te ~ 10 eV) plasma experiment; the HNBP is a variation on the well known Heavy Ion Beam Probe (HIBP) 2 The HNBP consists of a Na ion beam that was chopped (modulated) before entering a heat pipe neutralizer consisting of Cs vapor; the beam next enters a toroidal magnetic field region where the probing atoms are ionized; the Na ions then follow a path to a calibrated energy analyzer. The calibration and first results have been obtained and will be presented. The Texas Helimak experiment approximates a sheared cylindrical slab intended to study turbulent-driven transport. The dominant magnetic field is toroidal with a weak vertical component. Vertical propagation of turbulence in Helimak is on the order of 1000 m/s and radial electric fields are thought to play a strong role. The HNBP is capable of radial potential measurements, shedding light on sheared flow turbulence. Heavy ion beam probing of low temperature plasmas is difficult because the signal depends on electron impact ionization of the probing beam. With low electron temperature and density (ne ~ 1011 cm-3), the signal expected was in the range of 1-10 nA, and the pure electronics noise was ~2 nA (rms). Because of the very low signal levels that were expected, a phase-sensitive signal processing scheme was implemented to recover the signal from the noise. The modulation was initially implemented as a triangular sweep in order to locate the signal. Once found, an on-off modulation scheme was implemented to increase total signal strength. The detected signal and interpretation will be presented along with noise characterization.

Summary form only given. The Heavy Neutral Beam Probe (HNBP) for the Helimak low temperature plasma experiment has been developed at the University of Texas at Austin (UT-Austin). The HNBP is based on the highly successful Heavy Ion Beam Probe (HIBP), but is engineered to work in the low temperature plasma regime (Te < 40 eV). The greatest difficutly to operation at low electron temperatures is that the measurement signal is enabled by electron-impact ionization events, which become increasingly rare when the temperature dips to ∼10 eV. This problem is overcome by probing the plasma with a neutral alkali metal (Na) and by modulating the probing beaming with a square wave (chopping) and recovering the signal with phase sensitive detection.

Robustness is a key issue on any runtime system that aims to speed up the execution of a program. However, robustness considerations are commonly overlooked when new software-based, thread-level speculation (STLS) systems are proposed. This paper highlights the relevance of the problem, showing different situations when the use of incorrect data can irreversibly alter the speculative execution of an algorithm, despite the efforts of a given STLS system to maintain sequential consistency. We show that the management of speculative exceptions is a common factor to these problems. Based on this fact, we propose a novel solution to handle speculative exceptions. Our solution eagerly tries to solve the issue before the non-speculative thread arrives to the instruction that rose the exception. We compare our solution to a more conservative approach found in the bibliography. The comparison is done both qualitatively, through a detailed analysis of the tradeoffs involved, and quantitatively, evaluating the effects of both solutions in the execution of three different benchmarks on a real system. Both studies conclude that our solution handles the occurrence of speculative exceptions more efficiently. Under heavy loads intended to push to its limits a STLS system, our solution leads to execution times reduced by up to 52.02% with respect to earlier proposals. Our solution does not affect the performance when speculative exceptions do not appear. We believe that our proposal makes STLS systems robust enough to be used in production environments.

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. Garcia-Martin, A. Hauptmann, and J. M. Martínez "People detection based on appearance and motion models", in 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2011, p. 256-260, The main contribution of this paper is a new people detection algorithm based on motion information. The algorithm builds a people motion model based on the Implicit Shape Model (ISM) Framework and the MoSIFT descriptor. We also propose a detection system that integrates appearance, motion and tracking information. Experimental results over sequences extracted from the TRECVID dataset show that our new people motion detector produces results comparable to the state of the art and that the proposed multimodal fusion system improves the obtained results combining the three information sources., This work has been partially supported by the Cátedra UAM-Infoglobal ("Nuevas tecnologías de vídeo aplicadas a sistemas de video-seguridad") and by the Universidad Autónoma de Madrid (“FPI-UAM: Programa propio de ayudas para la Formación de Personal Investigador”)

This article describes the simulation and analysis of a collisionless optical interconnection network with single protocol control. The optical fibre coupler has one shared control channel and N communication channels. Each network node has two communication modules, one for packet transmission/reception and the other for control channel access. We show by simulation that the system achieves a high performance and ensures high scalability.