Abstract
In this paper, an algorithm is proposed to detect the abnormal event in the form of rectangular blob in global images. Observing the status of the varying blobs, unusual behavior can be monitored and alarmed. A method extracting blobs from crowded video scenes is proposed, the covariance matrix descriptor fuses the image intensity and the optical flow to encode moving information and image characteristics of a blob. After characterizing normal behaviors of blobs or frames in a learning period, the nonlinear one-class SVM algorithm locates the abnormal blobs intra frame. The method is applied to detect abnormal events on several video surveillance datasets, and get promising results.
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Acknowledgment
This work is partially supported by the SURECAP CPER project and the Platform CAPSEC funded by REGION CHAMPAGNE-ARDENNE and FEDER, the Fundamental Research Funds for the Central Universities and the National Natural Science. Foundation of China (Grant No. U1435220, No. 61365003).
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Wang, T., Lai, K., Li, C., Snoussi, H. (2015). Joint Abnormal Blob Detection and Localization Under Complex Scenes. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_28
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DOI: https://doi.org/10.1007/978-3-319-22180-9_28
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