Multiple object tracking with kernel particle filter

A new particle filter, kernel particle filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 566 - 573 vol. 1
Main Authors Cheng Chang, Ansari, R., Khokhar, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Application software
Computer vision
Density functional theory
Image sequences
Kernel
Particle filters
Particle tracking
Robustness
Sampling methods
State-space methods
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Summary:A new particle filter, kernel particle filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate. A data association technique is also proposed to resolve the motion correspondence ambiguities that arise when multiple objects are present. The data association technique introduces minimal amount of computation by making use of the intermediate results obtained in particle allocation. We show that KPF performs robust multiple object tracking with improved sampling efficiency.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.243