Abstract The correlation filtering algorithm based on spatial regularization cannot suppress the weight of the background region precisely. The credibility of results is reduced by the factors like occlusion and deformation. A spatially abnormal adaptive target tracking is proposed to solve the problems. Firstly, an adaptive spatial regularization term is introduced, and its weight is initialized by significance detection to realize the spatial adaptability. Secondly, the alternating direction method of multipliers is adopted to reduce the complexity of the algorithm. Finally, a verification score is set in each subsequent frame to calculate the reliability of detection result and analyze the abnormal situation. A dynamic update rate for the target model is set and thus the abnormal adaptability is realized. Experiments on four public datasets show that the proposed algorithm produces a good tracking result in different complex scenes, such as deformation, occlusion and illumination variation, and it basically meets the real-time requirements.
Fund:National Natural Science Foundation of China(No.61172144), Natural Science Foundation of Liaoning Province(No.20170540426), Education Department of Liaoning Prov-ince(No.LJYL049)
Corresponding Authors:
JIANG Wentao, Ph.D., associate professor. His research interests include image processing, pattern re-cognition and artificial intelligence.
About author:: LIU Xiaoxuan, master student. Her research interests include image processing, pattern recognition and artificial intelligence.TU Chao, master student. His research interests include image processing, pattern re-cognition and artificial intelligence.JIN Yan, master student. His research interests include image processing, pattern re-cognition and artificial intelligence.
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