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The Rural Postman Problem with Temporal Unavailability (RPP-TU) is a variant of the Rural Postman Problem (RPP) specified for multi-agent planning over directed graphs with temporal constraints. These temporal constraints represent the unavailable time intervals for each arc during which agents cannot traverse the arc. Such arc unavailability scenarios occur in routing and scheduling of the instrumented wagons for inspection of railway tracks without disturbing the train schedules, i.e. the scheduled trains prohibit access to the signal blocks (sections of railway track separated by signals) for some finite interval of time. A three-index formulation for the RPP-TU is adopted from the literature. The three-index formulation has binary variables for describing the route information of the agents, and continuous non-negative variables to describe the schedules at pre-defined locations. A relaxation of the three-index formulation for RPP-TRU, referred to as Cascaded Graph Formulation (CGF), is investigated in this work. The CGF has attributes that simplify the polyhedral study of time-dependent arc routing problems like RPP-TRU. A novel branch-and-cut algorithm is proposed to solve the RPP-TU, where branching is performed over the service arcs. A family of facet-defining inequalities, derived from the polyhedral study, is used as cutting planes in the proposed branch-and-cut algorithm to reduce the computation time by up to $48\%$. Finally, an application of this work is showcased using a simulation case study of a railway inspection scheduling problem based on Kurla-Vashi-Thane suburban network in Mumbai, India. An improvement of $93\%$ is observed when compared to a Benders' decomposition based MILP solver from the literature., Comment: Visual proof in appendix

In the competitive realm of sports, optimal performance necessitates rigorous management of nutrition and physical conditioning. Specifically, in badminton, the agility and precision required make it an ideal candidate for motion analysis through video analytics. This study leverages advanced neural network methodologies to dissect video footage of badminton matches, aiming to extract detailed insights into player kinetics and biomechanics. Through the analysis of stroke mechanics, including hand-hip coordination, leg positioning, and the execution angles of strokes, the research aims to derive predictive models that can suggest improvements in stance, technique, and muscle orientation. These recommendations are designed to mitigate erroneous techniques, reduce the risk of joint fatigue, and enhance overall performance. Utilizing a vast array of data available online, this research correlates players' physical attributes with their in-game movements to identify muscle activation patterns during play. The goal is to offer personalized training and nutrition strategies that align with the specific biomechanical demands of badminton, thereby facilitating targeted performance enhancements., Comment: 7 pages, 11 figures. https://ieeexplore.ieee.org/document/9825164

Generic Event Boundary Detection (GEBD) task aims to recognize generic, taxonomy-free boundaries that segment a video into meaningful events. Current methods typically involve a neural model trained on a large volume of data, demanding substantial computational power and storage space. We explore two pivotal questions pertaining to GEBD: Can non-parametric algorithms outperform unsupervised neural methods? Does motion information alone suffice for high performance? This inquiry drives us to algorithmically harness motion cues for identifying generic event boundaries in videos. In this work, we propose FlowGEBD, a non-parametric, unsupervised technique for GEBD. Our approach entails two algorithms utilizing optical flow: (i) Pixel Tracking and (ii) Flow Normalization. By conducting thorough experimentation on the challenging Kinetics-GEBD and TAPOS datasets, our results establish FlowGEBD as the new state-of-the-art (SOTA) among unsupervised methods. FlowGEBD exceeds the neural models on the Kinetics-GEBD dataset by obtaining an F1@0.05 score of 0.713 with an absolute gain of 31.7% compared to the unsupervised baseline and achieves an average F1 score of 0.623 on the TAPOS validation dataset., Comment: Accepted in WACV-2024. Supplementary at https://openaccess.thecvf.com/content/WACV2024/supplemental/Gothe_Whats_in_the_WACV_2024_supplemental.pdf

In an environment with certain locations of higher priority, it is required to patrol these locations as frequently as possible due to their importance. However, the Non-Priority locations are often neglected during the task. It is necessary to balance the patrols on both kinds of sites to avoid breaches in security. We present a distributed online algorithm that assigns the routes to agents that ensures a finite time visit to the Non-Priority locations along with Priority Patrolling. The proposed algorithm generates offline patrol routes (Rabbit Walks) with three segments (Hops) to explore non-priority locations. The generated number of offline walks depends exponentially on a parameter introduced in the proposed algorithm, thereby facilitating the scalable implementation based on the onboard resources available on each patrolling robot. A systematic performance evaluation through simulations and experimental results validates the proportionately balanced visits and suggests the proposed algorithm's versatile applicability in the implementation of deterministic and non-deterministic scenarios., Comment: 8 pages, 8 figures, 2 tables

In this paper, we present a novel swarm algorithm, swarm synergy, designed for robots to form communities within a swarm autonomously and anonymously. These communities, characterized as clusters of robots, emerge without any (pre-defined or communicated) specific locations. Each robot operates as a silent agent, having no communication capability, making independent decisions based on local parameters. The proposed algorithm allows silent robots to achieve this self-organized swarm behavior using only sensory inputs from the environment. The robots intend to form a community by sensing the neighbors, creating synergy in a bounded environment. We further infer the behavior of swarm synergy to ensure the anonymity/untraceability of both robots and communities and show the results on dynamicity of various parameters relevant to swarm communities such as community size, community location, number of community, no specific agent structure in the community, etc. The results are further analysed to observe the effect of sensing limitations posed by the onboard sensor's field of view. Simulations and experiments are performed to showcase the algorithm's scalability, robustness, and fast convergence. Compared to the state-of-art with similar objectives, the proposed communication-free swarm synergy shows comparative time to synergize or form communities. The proposed algorithm finds applications in studying crowd dynamics under high-stress scenarios such as fire, attacks, or disasters., Comment: 22 Pages, 8 figures, 6 tables, pre-print version

Pedestrian detection has become a cornerstone for several high-level tasks, including autonomous driving, intelligent transportation, and traffic surveillance. There are several works focussed on pedestrian detection using visible images, mainly in the daytime. However, this task is very intriguing when the environmental conditions change to poor lighting or nighttime. Recently, new ideas have been spurred to use alternative sources, such as Far InfraRed (FIR) temperature sensor feeds for detecting pedestrians in low-light conditions. This study reviews recent developments in low-light pedestrian detection approaches. It systematically categorizes and analyses various algorithms from region-based to non-region-based and graph-based learning methodologies by highlighting their methodologies, implementation issues, and challenges. It also outlines the key benchmark datasets that can be used for research and development of advanced pedestrian detection algorithms, particularly in low-light situations.

Spherical robots can conduct surveillance in hostile, cluttered environments without being damaged, as their protective shell can safely house sensors such as cameras. However, lateral oscillations, also known as wobble, occur when these sphere-shaped robots operate at low speeds, leading to shaky camera feedback. These oscillations in a pendulum-actuated spherical robot are caused by the coupling between the forward and steering motions due to nonholonomic constraints. Designing a controller to limit wobbling in these robots is challenging due to their underactuated nature. We propose a model-based controller to navigate a pendulum-actuated spherical robot using wobble-free turning maneuvers consisting of circular arcs and straight lines. The model is developed using Lagrange-D'Alembert equations and accounts for the coupled forward and steering motions. The model is further analyzed to derive expressions for radius of curvature, precession rate, wobble amplitude, and wobble frequency during circular motions. Finally, we design an input-output feedback linearization-based controller to control the robot's heading direction and wobble. Overall, the proposed controller enables a teleoperator to command a specific forward velocity and pendulum angle as per the desired turning radius while limiting the robot's lateral oscillations to enhance the quality of camera feedback., Comment: The length of the research paper is 20 pages, and it contains 15 graphs or illustrations

A spherical robot has many practical advantages as the entire electronics are protected within a hull and can be carried easily by any Unmanned Aerial Vehicle (UAV). However, its use is limited due to finding mounts for sensors. Pendulum actuated spherical robot provides space for mounting sensors at the yoke. We study the non-linear dynamics of a pendulum-actuated spherical robot to analyze the dynamics of internal assembly (yoke) for mounting sensors. For such robots, we provide a coupled dynamic model that takes care of the relationship between forward and sideways motion. We further demonstrate the effects of wobbling and precession captured by our model when the bot is controlled to execute a turning maneuver while moving with a moderate forward velocity, a practical situation encountered by spherical robots moving in an indoor setting. A simulation setup based on the developed model provides visualization of the spherical robot motion., Comment: The paper has been accepted to the 22nd IFAC International Symposium on Automatic Control in Aerospace (ACA) 2022. It consists of 6 pages and 15 figures

In recent years, most of the deep learning solutions are targeted to be deployed in mobile devices. This makes the need for development of lightweight models all the more imminent. Another solution is to optimize and prune regular deep learning models. In this paper, we tackle the problem of CNN model pruning with the help of Self-Similarity Matrix (SSM) computed from the 2D CNN filters. We propose two novel algorithms to rank and prune redundant filters which contribute similar activation maps to the output. One of the key features of our method is that there is no need of finetuning after training the model. Both the training and pruning process is completed simultaneously. We benchmark our method on two of the most popular CNN models - ResNet and VGG and record their performance on the CIFAR-10 dataset., Comment: Paper accepted in the 7th International Conference on Computer Vision & Image Processing (2022)

In recent days, with increased population and traffic on roadways, vehicle collision is one of the leading causes of death worldwide. The automotive industry is motivated on developing techniques to use sensors and advancements in the field of computer vision to build collision detection and collision prevention systems to assist drivers. In this article, a deep-learning-based model comprising of ResNext architecture with SENet blocks is proposed. The performance of the model is compared to popular deep learning models like VGG16, VGG19, Resnet50, and stand-alone ResNext. The proposed model outperforms the existing baseline models achieving a ROC-AUC of 0.91 using a significantly less proportion of the GTACrash synthetic data for training, thus reducing the computational overhead., Comment: 8 pages, 5 figures, submitted to IEEE-SMC 2021

Fonts are one of the most basic and core design concepts. Numerous use cases can benefit from an in depth understanding of Fonts such as Text Customization which can change text in an image while maintaining the Font attributes like style, color, size. Currently, Text recognition solutions can group recognized text based on line breaks or paragraph breaks, if the Font attributes are known multiple text blocks can be combined based on context in a meaningful manner. In this paper, we propose two engines: Font Detection Engine, which identifies the font style, color and size attributes of text in an image and a Font Prediction Engine, which predicts similar fonts for a query font. Major contributions of this paper are three-fold: First, we developed a novel CNN architecture for identifying font style of text in images. Second, we designed a novel algorithm for predicting similar fonts for a given query font. Third, we have optimized and deployed the entire engine On-Device which ensures privacy and improves latency in real time applications such as instant messaging. We achieve a worst case On-Device inference time of 30ms and a model size of 4.5MB for both the engines., Comment: Accepted for publication in IEEE ICASSP 2021: 46th IEEE International Conference on Acoustics, Speech, & Signal Processing

Time-dependent (or temporal) properties may arise in many network-based planning problems, particularly in the routing and scheduling of railway track inspection problems. The availability of tracks depends on the train schedules, maintenance possessions, etc. In the absence of side constraints, this routing and scheduling problem is formulated as a multi-agent rural postman problem on a temporal-directed network; where a given set of rail track sections must be visited while respecting the temporal attributes due to railway track unavailabilities. In this work, we adopt a three-index formulation for the multi-agent Rural Postman Problem with Temporal Recurring Unavailabilities (RPP-TRU) and frame it as a Mixed Integer Linear Programming (MILP) problem. In addition, we propose relevant theoretical studies for RPP-TRU to ensure the feasibility of the proposed optimization problem. Two approaches of an exact algorithm are proposed, based on Benders' decomposition framework, to address the disjunctive unavailability constraints occurring in its scheduling sub-problems, alongside the NP-Hard routing (master) problem. A polynomial-time algorithm is designed to address the scheduling sub-problem, while the NP-Hard master problem is solved using MILP toolbox. Comparison results with RPP (without temporal constraints) show a minor compromise with the spatial cost solution with significantly less delay, hence suitable for real-world routing and scheduling applications occurring in a shared network like railways. A simulation study on a part of the Mumbai suburban railway network demonstrates the working of the proposed methodology under a realistic setting.

Phone level localization of mis-articulation is a key requirement for an automatic articulation error assessment system. A robust phone segmentation technique is essential to aid in real-time assessment of phone level mis-articulations of speech, wherein the audio is recorded on mobile phones or tablets. This is a non-standard recording set-up with little control over the quality of recording. We propose a novel post processing technique to aid Spectral Transition Measure(STM)-based phone segmentation under noisy conditions such as environment noise and clipping, commonly present during a mobile phone recording. A comparison of the performance of our approach and phone segmentation using traditional MFCC and PLPCC speech features for Gaussian noise and clipping is shown. The proposed approach was validated on TIMIT and Hindi speech corpus and was used to compute phone boundaries for a set of speech, recorded simultaneously on three devices - a laptop, a stationarily placed tablet and a handheld mobile phone, to simulate different audio qualities in a real-time non-standard recording environment. F-ratio was the metric used to compute the accuracy in phone boundary marking. Experimental results show an improvement of 7% for TIMIT and 10% for Hindi data over the baseline approach. Similar results were seen for the set of three of recordings collected in-house., Comment: 6 pages, 6 figures

Perception in 3D has become standard practice for a large part of robotics applications. High quality 3D perception is costly. Our previous work on a nodding 2D Lidar provides high quality 3D depth information with low cost, but the sparse data generated by this sensor poses challenges in understanding the characteristics of moving objects within an uncertain environment. This paper proposes a novel design of the nodding Lidar but provides dynamic reconfigurability in terms of limiting the field of view of the sensor using a set of optical mirrors. It not only provides denser scans, but it also achieves a three times higher scan update rate. Additionally, we propose a novel calibration mechanism for this sensor and prove its effectiveness for dynamic object detection and tracking.

Motion planning in the presence of multiple dynamic obstacles is an important research problem from the perspective of autonomous vehicles as well as space-constrained multi-robot work environment. In this paper, we address the motion planning problem for multiple dynamic obstacle rich environment and propose a probabilistically, complete novel motion planning algorithm. Our claim is that given a fixed path cost i.e. the Euclidean path length, the proposed algorithm plans a path with the least computational time as compared to the state-of-the-art techniques. At the same time, given the time duration for planning, it plans the minimum cost path. Dynamic constraints have been taken into consideration while designing the planner such that the optimal planned path is feasible for implementation. The results of extensive simulation experiments show that the proposed sequential BIT* outperforms the DOVS both in the planned path length as well as the path generation time., Comment: 8 pages, 10 figures

Vision based localization is a popular approach to carry out manoeuvres particularly in GPS-restricted indoor environments, because vision can complement other activities performed by the robot. The objective is to estimate the current location with respect to a known location by matching the bearings. The problem is challenging as the known location information is in terms of the bearings of landmarks extracted from an image. We address the problem under more challenging scenario when landmarks are semi-static. In this work, an observer formulation is presented which enables to incorporate the effect of change in landmark position as parameters. The efficacy of two estimators: Augmented Extended Kalman Filter (A-EKF) and a Proportional-Integral EKF (PI-EKF) is tested under the cases where there are changes in some of the landmark positions. Morever, it is likely that not all landmarks are visible to the robot at all instants of time. A multi-rate estimation framework is proposed to mitigate this issue. Observability analysis is carried out to arrive upon a minimum number of landmarks required for such a formulation. Simulation studies are presented to test the efficacy of the formulations., Comment: Submitted to the IFAC 21st World Congress, 2020, for review. 6 pages 6 figures

This paper proposes trajectory planning strategies for online reconfiguration of a multi-agent formation on a Lissajous curve. In our earlier work, a multi-agent formation with constant parametric speed was proposed in order to address multiple objectives such as repeated collision-free surveillance and guaranteed sensor coverage of the area with ability for rogue target detection and trapping. This work addresses the issue of formation reconfiguration within this context. In particular, smooth parametric trajectories are designed for the purpose using calculus of variations. These trajectories have been employed in conjunction with a simple local cooperation scheme so as to achieve collision-free reconfiguration between different Lissajous curves. A detailed theoretical analysis of the proposed scheme is provided. These surveillance and reconfiguration strategies have also been validated through simulations in MATLAB\reg for agents performing parametric motion along the curves, and by Software-In-The-Loop simulation for quadrotors. In addition, they are validated experimentally with a team of quadrotors flying in a motion capture environment.