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《Optical Technique》 2018-05
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Compressed sensing object tracking based on weighted multiple instance learning

YANG Yuesheng;WANG Dongli;ZHOU Yan;College of Information Engineering,Xiangtan University;  
In order to handle occlusion,illumination change and background clutter,a novel target tracking method adopting compressed sensing and weighted multiple instance learning is proposed.After extracting Haar-like features of an image patch,the dimension of these features is reduced by random projection.Combining weighted multiple instance learning,the tracked target patch is located as the classifier's maximum respond value in the framework of boosting learning.The target in the first frame is labeled manually,and the Haar-like features extracted from the positive and negative instances is reduced for the sequence frames and the tracking patch is obtained by training classifiers.Experimental results illustrate the accuracy,real-time and robustness of the proposed method,for the selected four challenging video sequence,the tracking success rate can over 89% and frame rate over 26 f/s.
【Fund】: 国家自然科学基金(61100140 61104210 61773330);; 湖南省自然科学基金(2017JJ2253);; 湖南省教育厅优秀青年项目(17B259)
【CateGory Index】: TP391.41
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