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Visual tracking is an important and active research area
in computer vision. Usually a similarity measure between two
probability distributions is used for tracking. The
commonly used similarity measures such as Kullback-Leibler distance
and Bhattacharyya distance are limited to one or two feature
dimensions, due to the difficulty in estimating the entropy of the
high-dimensional features. We proposed a similarity measure which is
the sum of all pair-wise kernelized distances between two
distributions. We used our similarity measure and mean-shift technique
to track single object in a joint feature-spatial space, and
achieved more accurate and robust tracking performance.
For multiple object tracking, we used distinctive features and
particle filter framework to simultaneously and reliably track
them. The quasi-random sampling and efficient distinctive
features are used to achieve a realtime tracking speed.
- C. Yang, R. Duraiswami and L. Davis. Efficient Spatial-Feature Tracking via the Mean-Shift and a New Similarity Measure. To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, June 2005.
- C. Yang, R. Duraiswami and L. Davis. Robust and Efficient Object Tracking Based on the Particle Filter. Submitted for publication, 2005.
- B. Han, C. Yang, R. Duraiswami and L. Davis. Bayesian Filtering and Integral Image for Visual Tracking. Invited to special session of Real-Time Object Tracking: Algorithms and Evaluation in Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Montreux, Switzerland, 2005.
- C. Yang, R. Duraiswami, A. Elgammal and L. Davis. On-Line Kernel-Based Tracking in Joint Feature-Spatial Spaces. DEMO on IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004.
- C. Yang, R. Duraiswami, A. Elgammal and L. Davis. Real-Time Kernel-Based Tracking in Joint Feature-Spatial Spaces. Technical Report CS-TR-4567, Dept. of Computer Science, University of Maryland, College Park, 2004.
Some sequences in the paper :
1. Ball sequence. (1.4MB)
2. Walking sequence. (1.1MB)
3. Predator sequence. (8.1MB)
4. Pedestrain sequence. (1.1MB)