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Proceedings Paper

Distribution majorization of corner points by reinforcement learning for moving object detection
Author(s): Hao Wu; Hao Yu; Dongxiang Zhou; Yongqiang Cheng
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Paper Abstract

Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.

Paper Details

Date Published: 13 April 2018
PDF: 7 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960C (13 April 2018); doi: 10.1117/12.2309525
Show Author Affiliations
Hao Wu, National Univ. of Defense Technology (China)
Hao Yu, National Univ. of Defense Technology (China)
Dongxiang Zhou, National Univ. of Defense Technology (China)
Yongqiang Cheng, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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