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|Title:||Probability hypothesis density approach for multi-camera multi-object tracking|
|Citation:||Pham, N.T.,Huang, W.,Ong, S.H. (2007). Probability hypothesis density approach for multi-camera multi-object tracking. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4843 LNCS (PART 1) : 875-884. ScholarBank@NUS Repository.|
|Abstract:||Object tracking with multiple cameras is more efficient than tracking with one camera. In this paper, we propose a multiple-camera multiple-object tracking system that can track 3D object locations even when objects are occluded at cameras. Our system tracks objects and fuses data from multiple cameras by using the probability hypothesis density filter. This method avoids data association between observations and states of objects, and tracks multiple objects in single-object state space. Hence, it has lower computation than methods using joint state space. Moreover, our system can track varying number of objects. The results demonstrate that our method has a high reliability when tracking 3D locations of objects. © Springer-Verlag Berlin Heidelberg 2007.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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