Abstract：The existing spatio-temporal context based target tracking algorithms have good performance for static occlusion due to the consideration of the spatio-temporal relationship between the object and the background. However, the large occlusion area of the object or the occluded fast-moving object still easily lead to inaccurate tracking or lost tracking. A target tracking algorithm combining target occlusion detection and context information is proposed in this paper. Firstly, the compressed illumination-invariant color features extracted from the first frame are utilized to constitute and initialize spatio-temporal context model. Then, the occlusions in the inputted video frames are judged by bidirectional trajectory error. If the bidirectional matching error of key points in object region between consecutive frames is less than a set threshold, there is no dynamic occlusion or severe static occlusion. Accordingly, the accurate tracking is conducted in virtue of spatio-temporal context model. Otherwise, the objects in the subsequent frames are detected by the combined classifiers until the objects can be detected again. Meanwhile, the context model and classifiers are updated online. The experimental results on several video frame sequences show that the proposed method can deal with severe static occlusion and dynamic occlusion in complex scenario well.
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2017, Vol. 30 
