TCSVT 2008 – Efficient Multi-Target Visual Tracking Using Random Finite Sets

| August 13, 2008

Emilio Maggio, Murtaza Taj, Andrea Cavallaro
IEEE Transaction on Circuits System and Video Technology, 2008


We propose a filtering framework for multi-target tracking that is based on the Probability Hypothesis Density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest. The PHD filter compensates for missing detections and removes noise and clutter. Moreover, this filter reduces the growth in complexity with the number of targets from exponential to linear by propagating the first-order moment of the multitarget posterior, instead of the full posterior. In order to account for the nature of the PHD propagation, we propose a novel particle resampling strategy and we adapt the dynamic and observation models to cope with varying object scales. The proposed resampling strategy allows us to use the PHD filter when a priori knowledge of the scene is not available. Moreover, the dynamic and observation models are not limited to the PHD filter and can be applied to any Bayesian tracker that can handle State Dependent Variances (SDV). Extensive experimental results on a large standard video surveillance dataset using a standard evaluation protocol show that the proposed filtering framework improves the accuracy of the tracker, especially in cluttered scenes.


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Text Reference:

E. Maggio, M. Taj and A. Cavallaro,
"Efficient multi-target visual tracking using random finite sets",
in IEEE Trans. Circuits Syst. Video Technol., vol. 18, no. 8, pp. 1016-1027, August 2008

Bibtex Reference:

 title={Efficient multi-target visual tracking using Random Finite Sets},
 author={E. Maggio and M. Taj and A. Cavallaro},


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Category: Journal Papers, Publications

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