Combining JPDA and particle filter for visual tracking

Merging and splitting of objects cause challenges for visual tracking. This is due to observation ambiguity, object lost, and tracking errors when objects are close together. In this paper, we propose a method to combine the joint probabilistic data association (JPDA) and the particle filter to main...

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Bibliographic Details
Published in2010 IEEE International Conference on Multimedia and Expo pp. 1044 - 1049
Main Authors Nam Trung Pham, Leman, Karianto, Wong, Melvin, Feng Gao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
data fusion
detection
Image color analysis
Joints
JPDA
Markov processes
Merging
Noise
particle filter
Vehicles
visual tracking
Visualization
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Summary:Merging and splitting of objects cause challenges for visual tracking. This is due to observation ambiguity, object lost, and tracking errors when objects are close together. In this paper, we propose a method to combine the joint probabilistic data association (JPDA) and the particle filter to maintain tracks of objects. The results of JPDA are employed to improve the observation model in the particle filter. Based on the ability of handling missing detections and clutter of JPDA, tracks of objects can be maintained after merging or splitting. Conversely, the particle filter also improves the performance of JPDA by fusing other observations such as color and background subtraction information. Hence, our method can take advantages from both JPDA and particle filter to track objects through merging and splitting.
ISBN:9781424474912
1424474914
ISSN:1945-7871
1945-788X
DOI:10.1109/ICME.2010.5583098