Integrating Shape and Color Features for Adaptive Real-time Object Tracking

Abstract

We extend the standard mean shift tracking algorithm to an adaptive tracker by selecting reliable features from color and shape cues. The standard mean shift algorithm assumes that the representation of tracking targets is always sufficiently discriminative enough against background. Most tracking algorithms developed based on the mean shift algorithm use only one cue (such as color) throughout their tracking process. The widely used color features are not always discriminative enough for target localization because illumination and viewpoint tend to change. Moreover, the background may be of a color similar to that of the target. We present an adaptive tracking algorithm that integrates color and shape features. Good features are selected and applied to represent the target according to the descriptive ability of these features. The proposed method has been implemented and tested in different kinds of image sequences. The experimental results demonstrate that our tracking algorithm is robust and efficient in the challenging image sequences.