Visual tracking of numerous targets via multi-Bernoulli filtering of image data

Authors:
Reza Hoseinnezhad;Ba-Ngu Vo;Ba-Tuong Vo;David Suter
Affiliations:
RMIT University, Victoria, Australia;The University of Western Australia, WA, Australia;The University of Western Australia, WA, Australia;The University of Adelaide, SA, Australia
Venue:
Pattern Recognition
Year:
2012

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Abstract

This paper presents a novel Bayesian method to track multiple targets in an image sequence without explicit detection. Our method is formulated based on finite set representation of the multi-target state and the recently developed multi-Bernoulli filter. Experimental results on sport player and cell tracking studies show that our method can automatically track numerous targets, and it outperforms the state-of-the-art in terms of false positive (false alarm) and false negative (missing) rates as detection error measures, and in terms of label switching rate and lost tracks ratio as tracking error measures.