Detection of Anomalies in Human Action Using Optical Flow and Gradient Tensor

Mishra, Soumya Ranjan; Mishra, Tusar Kanti; Sarkar, Anirban; Sanyal, Goutam

doi:10.1007/978-981-13-9282-5_53

Cited by 11 publications

(1 citation statement)

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“…One such method is based on crowd flow which uses information about motion of crowd. In (Wang et al,2014;Mishra et al,2020) abnormal event detection is carried out by modelling optical flow, which tracks change in arrangement of pixel and does not require object tracking. Optical flow based algorithms are capable of easily and precisely modeling highly crowed scene.…”

Section: Introductionmentioning

confidence: 99%

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

Singh

Kapoor

Khosla

2021

International Journal of Cognitive Informatics and Natural Intelligence

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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.

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Section: Introductionmentioning

confidence: 99%