13 April 2018 Distribution majorization of corner points by reinforcement learning for moving object detection
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960C (2018) https://doi.org/10.1117/12.2309525
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
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.
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Hao Wu, Hao Wu, Hao Yu, Hao Yu, Dongxiang Zhou, Dongxiang Zhou, Yongqiang Cheng, Yongqiang Cheng, "Distribution majorization of corner points by reinforcement learning for moving object detection", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960C (13 April 2018); doi: 10.1117/12.2309525; https://doi.org/10.1117/12.2309525

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