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Distributed tracking in wireless smart-camera networks is affected by varying local processing delays that generally depend on the current scene complexity. As a consequence, each camera makes target information available to the network at different time instants. These unknown delays compound the drifts caused by local clocks and may induce tracking failures when target information is fused. To address...
Target tracking in a network of wireless cameras may fail if data are captured or exchanged asynchronously. Unlike traditional sensor networks, video processing may generate significant delays that also vary from camera to camera. Moreover, the continuous and rapid change of the dynamics of the consensus variable (the target state) makes tracking even more challenging under these conditions. To address...
We propose N-consensus, an algorithm that reduces the cost of the consensus process for distributed visual target tracking without compromising on tracking accuracy. N-consensus fuses target posteriors computed by viewing nodes (i.e. the cameras viewing the same target) only and limits the number of nodes participating in consensus to those within a specified number of hops from the viewing nodes...
Consensus-based target tracking in camera networks faces three major problems: non-linearity in the measurement model, temporary lack of measurements (naivety) due to the limited field of view (FOV) and redundancy in the iterative exchange of information. In this paper we propose two consensus-based distributed algorithms for non-linear systems using the Extended Information Filter as underlying filter...
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