[1]NING Xin,LI Weijun,TIAN Weijuan,et al.Adaptive template update of discriminant KCF for visual tracking[J].CAAI Transactions on Intelligent Systems,2019,14(1):121-126.[doi:10.11992/tis.201806038]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 1
Page number:
121-126
Column:
学术论文—机器学习
Public date:
2019-01-05
- Title:
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Adaptive template update of discriminant KCF for visual tracking
- Author(s):
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NING Xin1; 2; LI Weijun1; 2; 3; TIAN Weijuan2; XU Chi2; XU Jian1
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1. Laboratory of Artificial Neural Networks and High-speed Circuits, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;
2. Image Cognitive Computing Joint Lab, Wave Group, Beijing 100083, China;
3. School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100029, China
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- Keywords:
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visual tracking; object detection; high-speed kernelized correlation filters; template update; convolution neural network
- CLC:
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TP391
- DOI:
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10.11992/tis.201806038
- Abstract:
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To solve the challenges of in-plane/out-of-plane rotation (IPR/OPR), fast motion (FM), and occlusion (OCC), a new robust visual tracking framework of discriminant kernelized correlation filter (KCF) based on adaptive template update strategy is presented in this paper. Specifically, the proposed discriminant models were first used to determine the tracking validity and then a new adaptive template update strategy was introduced to effectively distinguish whether or not the object has rotated when the object tracking was abnormal. Furthermore, a new visual tracking framework combining object test is presented, which could further effectively distinguish FM and OCC. Meanwhile, to overcome the above-mentioned challenges, three measures were taken to recover the object tracking frame:template updating, object movement displacement minimization, and use of an object detection algorithm ensuring validity and long-term visual tracking. We implemented two versions of the proposed tracker with representations from two conventional hand-actuated features, histogram of oriented gradient (HOG), and color names (CN) to validate the strong compatibility of the algorithm. Experimental results demonstrated the state-of-the-art performance in tracking accuracy and speed for processing the cases of IPR/OPR, FM, and OCC.