Background light ray modeling for change detection |
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| Affiliation: | 1. Faculty of Arts and Science, Kyushu University, 819-0395, Japan;2. Faculty of Information Science and Electrical Engineering, Kyushu University, Japan;1. School of Information and Electronics, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, PR China;2. Department of Electronic Engineering, Chung Yuan Christian University, No. 200, Zhongbei Rd., Zhongli City, Taoyuan County 320, Taiwan, ROC;1. College of Information Science and Technology, Beijing Normal University, Beijing, China;2. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China;3. Banner Alzheimer’s Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA;1. Department of Biomedical Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, Jiangsu 211106, China;2. Department of Radiology, Guangdong Province Traditional Chinese Medical Hospital, Guangzhou 510006, China;1. Department of Mathematics and Physics, North China Electric Power University, China;2. School of Science, Communication University of China, China;1. Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China |
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| Abstract: | This paper is an extension of the work that was originally reported in Shimada et al. (2013). This paper proposes a change detection method based on spatio-temporal light ray consistency. The proposed method introduces light field sensing, which is used to generate an arbitrary in-focus plane. Change detection is performed in a surveillance scene, where the background region can be filtered out by an out-focusing process. This approach resolves a longstanding issue in background modeling-based object detection, which often suffers from false positives in the background regions. To realize this new change detection method, a new feature representation, called the local ray pattern (LRP), is introduced. The LRP evaluates the spatial consistency of the light rays, and this plays an important role in distinguishing whether the light rays come from the in-focus plane or elsewhere. A combination of the LRP and Gaussian mixture model (GMM)-based background modeling realizes change detection in the in-focus plane. Experimental results demonstrate the proposed method’s effectiveness and its applicability to video surveillance. |
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| Keywords: | Change detection Light field Background modeling Spatio-temporal consistency |
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