Real-Time Non-rigid Object Tracking Using CAMShift with Weighted Back Projection
Lei Sun, Bingrong Wang, Takeshi Ikenaga · 2010
The Continuously Adaptive Mean Shift algorithm (CAMShift) is an adaptation of mean shift algorithm for object tracking especially for head and face tracking. Traditional CAMShift can not deal with multi-colored object tracking and situations when similar colors exist nearby. In this paper, a new approach towards these problems using CAMShift with weighted back projection is proposed. In our approach, multi-dimensional histogram with thresholding strategy is utilized. And a new back projection weighting strategy is proposed for situations when similar colors exist near the tracked object. Through experiments, the results show that the proposed method exceeds the traditional CAMShift in situations with multi-colored object or similar-colored background while keeping the processing speed real-time.