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《Computer Engineering》 2015-01
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Object Tracking Algorithm Based on HOG and Multiple-instance Online Learning

LIU Zhe;CHEN Ken;ZHENG Ziwei;College of Information Science and Engineering,Ningbo University;  
In order to achieve effectively stabilized target tracking within partial occlusion,illumination changes and complex background environment,this paper presents an object tracking algorithm based on Histogram of Oriented Gradients(HOG)and Multiple-instance Learning(MIL). Using the HOG feature space of the target block and the background in the first frame with Local Binary Pattern(LBP)descriptor to initialize the initial random ferns,it detects the target location and the objective scale of each frame with random multiple-scale sampling and uses the new target samples and the nearby background samples to update the appearance model within multi-instance learning after each detection. Through the experiments,the algorithm with multiple online tracking algorithms such as Online Boosting Tracker and MILTracker are compared and analyzed in a number of video sequences. The results show that it has a good target tracking stability under the complex environment,especially with partial occlusion and illumination changes,but in the anti-rotation of target,the algorithm has yet to be optimized.
【Fund】: 国家科技重大专项基金资助项目(2011ZX03002-004-02);; 教育部高等学校博士学科点专项科研基金资助项目(20113305110002);; 浙江省重点科技创新团队基金资助项目(2012R10009-04);; 浙江省杰出青年科学基金资助项目(R1110416)
【CateGory Index】: TP391.41
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