Multiple targets tracking in infrared image sequences based on joint probabilistic data association

2011 ◽  
Author(s):  
Da Wu ◽  
Zhenming Peng
Keyword(s):  
Data Association ◽  
Infrared Image ◽  
Image Sequences ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association

Improved adaptive joint probabilistic data association filter for multiple targets angles tracking

1997 ◽  
Vol 33 (16) ◽  
pp. 1361 ◽  
Author(s):  
M. Keche ◽  
M.S. Woolfson ◽  
I. Harrison ◽  
A. Ouamri ◽  
S.S. Ahmeda
Keyword(s):  
Data Association ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association

Modified Joint Probability Data Association Algorithm Avoiding Track Coalescence

2012 ◽  
Vol 433-440 ◽  
pp. 2298-2303 ◽  
Author(s):  
Song Lin Chen ◽  
Yi Bing Xu
Keyword(s):  
Small Angle ◽  
Data Association ◽  
Joint Probability ◽  
Tracking Performance ◽  
Probabilistic Data ◽  
Multiple Targets ◽  
Probabilistic Data Association ◽  
Simulation Results ◽  
Exclusive Measurement ◽  
Association Algorithm

Joint Probabilistic Data Association has proven to be effective in tracking multiple targets from measurements amidst clutter and missed detections. But the traditional Joint Probabilistic Data Association algorithm will cause track coalescence when the targets are parallel neighboring or small-angle crossing. To avoid track coalescence, a modified Joint Probabilistic Data Association algorithm is proposed in this paper. An exclusive measurement is defined for every target in the new algorithm. The exclusive measurement of a target is one measurement which associates with the target and has the maximum associated probability. The associated events of the exclusive measurement with other targets will be pruned, which resists two or more targets share the same measurement as a key measurement and avoids track coalescence. The simulation results show that the new algorithm can effectively solve track coalescence problem in all kinds of scenarios and keep a high tracking performance.

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