Abnormal event detection in video using motion and appearance information
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This paper presents an approach for the detection and localization of abnormal events in pedestrian areas. The goal is to design a model to detect abnormal events in video sequences using motion and appearance information. Motion information is represented through the use of the velocity and acceleration of optical flow and the appearance information is represented by texture and optical flow gradient. Unlike literature methods, our proposed approach provides a general solution to detect both global and local abnormal events. Furthermore, in the detection stage, we propose a classification by local regions. Experimental results on UMN and UCSD datasets confirm that the detection accuracy of our method is comparable to state-of-the-art methods. © Springer International Publishing AG, part of Springer Nature 2018.
Computer vision , Feature extraction , Information use , Motion analysis , Optical flows , Security systems , Abnormal event detections , Detection accuracy , Detection and localization , Motion information , Spatio temporal features , State-of-the-art methods , Video analysis , Video surveillance , Pattern recognition