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Object Tracking Algorithm Based on and L2-regularization Least Square and Convolutional Networks
ZHOU Fei 1 *,XUE Bin 2,AN Kangning 2,GAO Jianjun 2
1.Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China;Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China;Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China;Chongqing Key Laboratory of Optical Communication and Networks, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
2.
*Correspondence author
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Funding: National Natural Science Foundation of China（No.No.61471077）
Opened online:16 April 2018
Accepted by: none
Citation: ZHOU Fei,XUE Bin,AN Kangning.Object Tracking Algorithm Based on and L2-regularization Least Square and Convolutional Networks[OL]. [16 April 2018] http://en.paper.edu.cn/en_releasepaper/content/4744496
 
 
Object tracking is a hot and difficult research topic in computer vision. In this paper, we propose a object tracking algorithm based on L2 regularization least squares method and convolution network under the particle filter framework. Firstly, the extent of occlusion can be evaluated by L2 tracker. Secondly, convolutional networks is used to locate the target object if the extent of occlusion satisfies two inequality constraints. In order to make convolutional networks suitable for tracking tasks with high real-time requirements, this thesis uses a simple two-layer convolutional networks to represent the targets robustly. Finally, most of the insignificant samples are removed before applying convolutional networks, which reduces the complexity of the algorithm. The experimental results on numerous challenging image sequences show that the proposed method is more robust and stable than L2 tracker when the target object undergoes dramatic appearance changes such as pose variation or rotation and is superior in accuracy to other classical tracking algorithms.
Keywords:Object tracking; Particle filter; L2-regularization; Convolution network
 
 
 

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