A Sparsity-Driven Approach to Multi-camera Tracking in Visual Sensor Networks

Serhan Cosar 1, 2, * Mujdat Cetin 2
* Auteur correspondant
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In this paper, a sparsity-driven approach is presented for multi-camera tracking in visual sensor networks (VSNs). VSNs consist of image sensors, embedded processors and wireless transceivers which are powered by batteries. Since the energy and bandwidth resources are limited, setting up a tracking system in VSNs is a challenging problem. Motivated by the goal of tracking in a bandwidth-constrained environment , we present a sparsity-driven method to compress the features extracted by the camera nodes, which are then transmitted across the network for distributed inference. We have designed special overcomplete dictionaries that match the structure of the features, leading to very parsimonious yet accurate representations. We have tested our method in indoor and outdoor people tracking scenarios. Our experimental results demonstrate how our approach leads to communication savings without significant loss in tracking performance.
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Communication dans un congrès
Workshop on Activity Monitoring by Multiple Distributed Sensing (AMMDS) in conjunction with 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, Aug 2013, Krakow, Poland. 2013
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Serhan Cosar, Mujdat Cetin. A Sparsity-Driven Approach to Multi-camera Tracking in Visual Sensor Networks. Workshop on Activity Monitoring by Multiple Distributed Sensing (AMMDS) in conjunction with 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, Aug 2013, Krakow, Poland. 2013. 〈hal-00919946〉

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