Efficient Mean-Shift Tracking via a New Similarity Measure

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-tocompute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorit...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Changjiang Yang, Ramani Duraiswami, Larry S. Davis
• Keywords: Classical Similarity Measures, Computer Vision, CVPR 2005, Discriminative Similarity Measure, Mean Shift Algorithm, Sample-based Classical Similarity, Spatial-feature Tracking Algorithm