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Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier

Gajendra Singh, Rajiv Kapoor, Arun Khosla

Source Title: International Journal of Cognitive Informatics and Natural Intelligence (IJCINI)15(3)

ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859840|DOI: 10.4018/IJCINI.20210701.oa2

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MLA

Singh, Gajendra, et al. "Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier." IJCINI vol.15, no.3 2021: pp.12-30. http://doi.org/10.4018/IJCINI.20210701.oa2

APA

Singh, G., Kapoor, R., & Khosla, A. (2021). Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(3), 12-30. http://doi.org/10.4018/IJCINI.20210701.oa2

Chicago

Singh, Gajendra, Rajiv Kapoor, and Arun Khosla. "Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.3: 12-30. http://doi.org/10.4018/IJCINI.20210701.oa2

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Abstract

Movement information of persons is a very vital feature for abnormality detection in crowded scenes. In this paper, a new method for detection of crowd escape event in video surveillance system is proposed. The proposed method detects abnormalities based on crowd motion pattern, considering both crowd motion magnitude and direction. Motion features are described by weighted-oriented histogram of optical flow magnitude (WOHOFM) and weighted-oriented histogram of optical flow direction (WOHOFD), which describes local motion pattern. The proposed method uses semi-supervised learning approach using combined classifier (KNN and K-Means) framework to detect abnormalities in motion pattern. The authors validate the effectiveness of the proposed approach on publicly available UMN, PETS2009, and Avanue datasets consisting of events like gathering, splitting, and running. The technique reported here has been found to outperform the recent findings reported in the literature.