M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
International Journal of Computer Vision
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Tracking Multiple People with a Multi-Camera System
WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Automatic Tracking of Human Motion in Indoor Scenes Across Multiple Synchronized Video Streams
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
AV16.3: an audio-visual corpus for speaker localization and tracking
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
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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.