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Solving Person Re-identification in Non-overlapping Camera using Efficient Gibbs Sampling
This video was recorded at British Machine Vision Conference (BMVC), Bristol 2013. This paper proposes a novel probabilistic approach for appearance-based person reidentification in non-overlapping camera networks. It accounts for varying illumination, varying camera gain and has low computational complexity. More specifically, we present a graphical model where we model the person's appearance in addition to camera illumination and gain. We analytically derive the solutions for the person's appearance and camera properties, and use a novel constant time Gibbs sampling scheme to estimate the identification labels. We validate our algorithm on two indoor datasets and perform a comparative analysis with existing algorithms. We demonstrate significantly increased re-identification accuracy in addition to significantly reducing the computational complexity on our datasets.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
