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.
Probability Hypothesis Density Approach for Multi-camera Multi-object Tracking
Computer Vision – ACCV 2007 ; 19 ; 875-884
Lecture Notes in Computer Science ; 4843 , 19
2007-01-01
10 pages
Article/Chapter (Book)
Electronic Resource
English
Multiple Object , Object Tracking , Gaussian Component , Multiple Camera , Probability Hypothesis Density Computer Science , Image Processing and Computer Vision , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Artificial Intelligence (incl. Robotics) , Biometrics , Algorithm Analysis and Problem Complexity
Human Motion Tracking The Gaussian Mixture Probability Hypothesis Density Filter Approach
BASE | 2010
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