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| Target Tracking Based on Occlusion Detection and Spatio-Temporal Context Information |
| CHU Jun, ZHU Tao, MIAO Jun, JIANG Landa |
| Institute of Computer Vision, Nanchang Hangkong University, Nanchang 330063 |
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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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Received: 10 October 2016
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About author:: (CHU Jun(Corresponding author), born in 1967, Ph.D., professor. Her research inte-rests include image processing and analysis, pattern recognition, and computer vision.)
(ZHU Tao, born in 1990, master student. His research interests include computer vision object tracking.)
(MIAO Jun, born in 1979, Ph.D., lectu-rer. His research interests include computer vision, pattern recognition and machine learning.)
(JIANG Landa, born in 1992, master student.His research interests include machine learning and pattern recognition.) |
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2017, Vol. 30 
