Improved mean shift integrating texture and color features for robust real time object tracking
The subject of mean shift algorithm for tracking the location of an object using a color model has recently gained considerable interest. However, the use of a color model to represent the tracked object is very sensitive to clutter interference, illumination changes, and the influence of background. Therefore, the applicability of basic color-based mean shift tracking is limited in many real world complex conditions. In this paper, we present a modified adaptive mean shift tracking algorithm integrating a combination of texture and color features. We first suggest a new texture-based target representation based on spatial dependencies and co-occurrence distribution within interest target region for invariant target description, which is computed through so-scaled Haralick texture features. Then, to improve the tracking further, we propose an extension to the mean shift tracker where a combination of texture and color features are used as the target model. To be consistent to the scale change and complex non-rigid motions of the tracked target, we suggest to adapt the tracking window of the proposed algorithm with the real moving target mask at tracking over time. Many experimental results demonstrate the successful of target tracking using the proposed algorithm in many complex situations, where the basic mean shift tracker obviously fails. The performance of the proposed adaptive mean shift tracker is evaluated using the VISOR video Dataset, thermal infrared-acquired images sequences bench mark, and also some proprietary videos.
haralick texture features mean shift tracker visual tracking color density co-occurrence matrix moving object extraction