Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Integrated Person Tracking Using Stereo, Color, and Pattern Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Human tracking using multiple-camera-based head appearance modeling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Consistent labeling of tracked objects in multiple cameras with overlapping fields of view
IEEE Transactions on Pattern Analysis and Machine Intelligence
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One of the key techniques of multi-camera tracking systems is cross-view object tracking. Feature Matching (FM) and Field of View (FOV) based methods are adopted in conventional solutions towards this problem. However, FM is not computationally efficient and the results heavily depend on the parameter settings of the cameras. Therefore, it is not effective in practical applications. In addition, approaches based on FOV suffer from the delay of the detection of newly appeared objects. The results are not reliable if only consistent labelling is utilized. In this paper, we propose a novel scheme for cross-view object tracking based on Projective Invariants (PI) and FM. The experimental results show that, our method improves the performance of normal PI-based tracking algorithms. Especially, it provides accurate tracking performance in the case of multiple objects appear closely in the same area.