Abstract To enhance the tracking robustness of the correlation filter(CF) in occlusion and background clutter, an algorithm for object tracking with multiple memory learning and adaptive correlation filter based on subspace and histogram is proposed. CF cannot cope with target appearance difference of adjacent frames in the different periods using a single template. A strategy of random updates is proposed to learn multiple target templates and adapt to the variation of target. Several candidate targets are obtained by random updates and the representation coefficients of the previous frame is learned by subspace structure to synthetically judge the accuracy of current candidates. Since CF and subspace representations are sensitive to background clutter, the color histogram is introduced to achieve complementary appearance representation. The statistical histogram is used as the independent judgment basis to improve the accuracy of the algorithm in judging the candidate target. The experimental results on video sequences demonstrate that the proposed algorithm has the ability of anti-occlusion and anti-background interference.
Fund:Supported by National Natural Science Foundation of China (No.61672265,61373055), Industrialization Project of Jiangsu Education Department(No.JH10-28)
Corresponding Authors:
WU Xiaojun(Corresponding author), Ph.D., professor. His research interests include artificial intelligence,pattern recognition and computer vision.
About author:: FENG Fei, master student. Her research interests include object tracking and pattern recognition.XU Tianyang, Ph.D. candidate. His research interests include artificial intelligence and pattern recognition.
FENG Fei,WU Xiaojun,XU Tianyang. Object Tracking with Multiple Memory Learning and Adaptive Correlation Filter Based on Subspace and Histogram[J]. , 2018, 31(7): 612-624.
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2018, Vol. 31 
