Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Real-Time Tracking Using Trust-Region Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coordinating interactive vision behaviors for cognitive assistance
Computer Vision and Image Understanding
Mean Shift Parallel Tracking on GPU
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Active Structured Learning for High-Speed Object Detection
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Tensor-SIFT Based Earth Mover's Distance for Contour Tracking
Journal of Mathematical Imaging and Vision
Hi-index | 0.00 |
Histogram based real-time object tracking methods, like the Mean-Shift tracker of Comaniciu/Meer or the Trust-Region tracker of Liu/Chen, have been presented recently. The main advantage is that a suited histogram allows for very fast and accurate tracking of a moving object even in the case of partial occlusions and for a moving camera. The problem is which histogram shall be used in which situation. In this paper we extend the framework of histogram based tracking. As a consequence we are able to formulate a tracker that uses a weighted combination of histograms of different features. We compare our approach with two already proposed histogram based trackers for different historgrams on large test sequences availabe to the public. The algorithms run in real-time on standard PC hardware.