Abstract
A lot of methods of abnormal crowd motion detection in videos have been proposed in recent years. Most of them are still based on low semantic features, such gray value, velocity and gradient. Usually, the low-level features cannot represent discriminative information of the scene. In addition, former representations often ignore information in time or space dimension. Thus, it is necessary to establish representations with discriminative features and spatio-temporal information. In this work, a crowd abnormal detection framework is proposed. Slow feature analysis (SFA), which can provide high semantic inherent features, is adopted in representation. Besides, the effect of spatio-temporal information is added into the representation. We conduct extensive experiments on two datasets to demonstrate the effectiveness of proposed method. Experimental results suggest that our method improves the detection performance.
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Liu, S., Jin, Y., Tao, Y., Tang, X. (2017). A Novel Representation for Abnormal Crowd Motion Detection. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_21
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DOI: https://doi.org/10.1007/978-3-319-67777-4_21
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