Robust Mean Shift Tracking Based on Multi-Cue Integration

Hong Liu, Ze Yu, Hongbin Zha

Abstract: Color-based mean shift has been addressed as an effective and fast algorithm for tracking color blobs. This deterministic searching method suffers from low saturation color object, color clutter in backgrounds and complete occlusion for several frames. This paper proposes a direct motion-color integration method to solve the low saturation color problem and the color background clutter problem. Based on the direct cue integration, an occlusion handler that is able to deal with long term full occlusion is proposed to solve the complete occlusion problem as well. Moreover, motivated by the idea of tuning weight of each cue according to its performance, a method of adaptive multi-cue integration based mean shift is proposed. Weights of each cue are adjusted according to a quality function, which is used to evaluate the performance of each cue in the adaptive integration scheme. Extensive experiments show that this method can adapt the weight of individual cue efficiently, and increase the robustness of tracking in various conditions.