Learning Object-Level Spatio-Temporal Representation for Abnormal Event Detection

Jongmin Yu, Sejeong Lee, and Moongu Jeon


Object-level spatio-temporal representation, 3D deep convolutional neural network


In this paper, we propose an approach for abnormal event detection, using the object-level spatio-temporal representation. Our approach detects an abnormal event in complex scenes which contain objects classified in various categories. We compute the object-level 3D Region-of-interest (3D RoI) and extract object-level 3D volume. Then, the object-level 3D volume is inputted to 3D deep convolutional neural network (3D-DCNN) for detecting the abnormal event. In the experiments, we compare our method with several methods on our experimental dataset.

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