Improved Kernel-Based Object Tracking Under Occluded Scenarios

Abstract

AbstractA 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.KeywordsTarget ModelScale Invariant Feature TransformScale ChangePartial OcclusionScale Invariant Feature Transform FeatureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.