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dc.contributor.authorHoseinnezhad, R.
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorVu, T.N.
dc.contributor.editorYing Tan
dc.contributor.editorYuhui Shi
dc.contributor.editorYi Chai
dc.contributor.editorGuoyin Wang
dc.identifier.citationHoseinnezhad, R. and Vo, B. and Vu, T.N. 2011. Visual tracking of multiple targets by Multi-Bernoulli filtering of background subtracted image data, in Tan, Y. and Shi, Y. and Chai, Y. and Wang, G. (ed), Advances in Swarm Intelligence: Lecture Notes in Computer Science Part 2. 6729: pp. 509-518. Berlin, Heidelberg: Springer.

Most visual multi-target tracking techniques in the literature employ a detection routine to map the image data to point measurements that are usually further processed by a filter. In this paper, we present a visual tracking technique based on a multi-target filtering algorithm that operates directly on the image observations and does not require any detection nor training patterns. Instead, we use the recent history of image data for non-parametric background subtraction and apply an efficient multi-target filtering technique, known as the multi-Bernoulli filter, on the resulting grey scale image data. In our experiments, we applied our method to track multiple people in three video sequences from the CAVIAR dataset. The results show that our method can automatically track multiple interacting targets and quickly finds targets entering or leaving the scene.

dc.subjectmulti-target tracking
dc.subjectBayesian estimation
dc.subjectrandom finite sets
dc.subjectvisual tracking
dc.titleVisual tracking of multiple targets by Multi-Bernoulli filtering of background subtracted image data
dc.typeBook Chapter
dcterms.source.titleAdvances in Swarm Intelligence Lecture Notes in Computer Science Volume 6729
dcterms.source.placeBerlin Heidelberg
curtin.accessStatusFulltext not available

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