Variational Bayesian Framework for Multi-Person Tracking
Sileye Ba, Yutong Ban, Xavier Alameda-PIneda, Alessio Xompero, and Radu Horaud
Papers | Matlab code | Results
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 work, 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 made possible thanks 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.
Detections (Observations) on MOT Challenge dataset
Tracking results on MOT Challenge dataset
Sileye Ba, Xavier Alameda-Pineda, Alessio Xompero, Radu Horaud. An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes. Computer Vision and Image Understanding, 2016, 153, pp.64-76. <10.1016/j.cviu.2016.07.006>
Yutong Ban, Sileye Ba, Xavier Alameda-Pineda, Radu Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. ECCV Workshop on Benchmarking Mutliple Object Tracking, Oct 2016, Amsterdam, Netherlands.
Official benchmark results of the MOTChallenge 2016
Detailed results obtained with the MOT’16 dataset : OVBT Tracker