Visual tracking based on adaptive multi-cue integration

The traditional color-based mean shift tracking algorithm is unable to accurately track the object. To address the problems, this paper presents an improved tracking algorithm. The improved tracker integrates the color and motion cues which characterize the appearance and motion information of object respectively. During the tracking process, this integration strategy can dynamically adjust the weight of each visual cue according to their reliability, thus these two visual cues can complement each other and make for more precise target localization. The experimental results demonstrate that the proposed tracking algorithm improves the tracking performance of the traditional mean shift tracker in complicated real scenarios.