Abstract
Surveillance cameras are expected to play a large role in the development of ITS technologies. They can be used to detect abnormally behaving individuals which can then be reported to drivers nearby. There are multiple works that tackle the problem of abnormal behavior detection. However, most of these works make use of appearance features which have redundant information and are susceptible to noise. While there are also works that make use of pose skeleton representation, they do not consider well how to handle cases with occlusions, which can occur due to the simple reason of pedestrian orientation preventing some joints from appearing in the frame clearly. In this paper, we propose a skeleton trajectory representation that enables handling of occlusions. We also propose a framework for pedestrian abnormal behavior detection that uses the proposed representation and detect relatively hard-to-notice anomalies such as drunk walking. The experiments we conducted show that our method outperforms other representation methods.
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Parts of this research were supported by MEXT, Grants-in-Aid for Scientific Research.
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Temuroglu, O. et al. (2020). Occlusion-Aware Skeleton Trajectory Representation for Abnormal Behavior Detection. In: Ohyama, W., Jung, S. (eds) Frontiers of Computer Vision. IW-FCV 2020. Communications in Computer and Information Science, vol 1212. Springer, Singapore. https://doi.org/10.1007/978-981-15-4818-5_9
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DOI: https://doi.org/10.1007/978-981-15-4818-5_9
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