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Tracking Multiple Persons Based on a Variational Bayesian Model

Yutong Ban 1 Sileye Ba 1, 2 Xavier Alameda-Pineda 3 Radu Horaud 1
1 PERCEPTION [2016-2019] - Interpretation and Modelling of Images and Videos [2016-2019]
Inria Grenoble - Rhône-Alpes, LJK [2016-2019] - Laboratoire Jean Kuntzmann [2016-2019], Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019]
Abstract : Object tracking is an ubiquitous problem in computer vision with many applications in human-machine and human-robot interaction, augmented reality, driving assistance, surveillance, etc. Although thoroughly investigated, tracking multiple persons remains a challenging and an open problem. In this paper, an online variational Bayesian model for multiple-person tracking is proposed. This yields a variational expectation-maximization (VEM) algorithm. The computational efficiency of the proposed method is due to closed-form expressions for both the posterior distributions of the latent variables and for the estimation of the model parameters. A stochastic process that handles person birth and person death enables the tracker to handle a varying number of persons over long periods of time. The proposed method is benchmarked using the MOT 2016 dataset.
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https://hal.inria.fr/hal-01359559
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Submitted on : Friday, September 2, 2016 - 4:24:13 PM
Last modification on : Friday, August 7, 2020 - 3:14:05 AM

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Yutong Ban, Sileye Ba, Xavier Alameda-Pineda, Radu Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. Computer Vision – ECCV 2016 Workshops, Oct 2016, Amsterdam, Netherlands. pp.52-67, ⟨10.1007/978-3-319-48881-3_5⟩. ⟨hal-01359559v2⟩

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