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

Jongmin Yu, Sejeong Lee, and Moongu Jeon


Objectlevel spatiotemporal 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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