Visual Tracking Algorithm Based on CAMSHIFT and Multi-cue Fusion for Human Motion Analysis

Ge Yang, Hong Liu

Abstract: It is still a challenging problem for tracking objects in complex visual situations, such as an object is occluded or the object's color features are very similar to its background. Therefore, a novel visual tracking algorithm is proposed for multiple cues fusion based on three common cues: color, target position prediction and motion continuity in this paper. Color feature is free of translation and rotation and robust to partial occlusions and pose variations. Features of target position prediction and motion continuity can handle the condition that the color difference between the foreground and the background is similar. Combining with CAMSHIFT (Continuously Adaptive Mean Shift) technique, experimental results show that the proposed visual tracking algorithm is more robust than traditional single cue and gets better tracking effect than CMST (Collaborative Mean Shift Tracking). Successful rates of the proposed algorithm are 70% to 100% in 4 different complex conditions.