Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shock Filters Based on Implicit Cluster Separation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Kernel-Based robust tracking for objects undergoing occlusion
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Dynamic multi-cue tracking with detection responses association
Proceedings of the international conference on Multimedia
Getting robust observation for single object tracking: a statistical Kernel-based approach
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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A successful approach for object tracking has been kernel based object tracking [1] by Comaniciu et al.. The method provides an effective solution to the problems of representation and localization in tracking. The method involves representation of an object by a feature histogram with an isotropic kernel and performing a gradient based mean shift optimization for localizing the kernel. Though robust, this technique fails under cases of occlusion. We improve the kernel based object tracking by performing the localization using a generalized (bidirectional) mean shift based optimization. This makes the method resilient to occlusions. Another aspect related to the localization step is handling of scale changes by varying the bandwidth of the kernel. Here, we suggest a technique based on SIFT features [2] by Lowe to enable change of bandwidth of the kernel even in the presence of occlusion. We demonstrate the effectiveness of the techniques proposed through extensive experimentation on a number of challenging data sets.