Abnormal Crowd Behavior Detection Using Motion Information Images and Convolutional Neural Networks

Date

2020

Author

Direkoğlu, Cem

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We introduce a novel method for abnormal crowd event detection in surveillance videos. Particularly, our work focuses on panic and escape behavior detection that may appear because of violent events and natural disasters. First, optical flow vectors are computed to generate a motion information image (MII) for each frame, and then MIIs are used to train a convolutional neural network (CNN) for abnormal crowd event detection. The proposed MII is a new formulation that provides a visual appearance of crowd motion. The proposed MIIs make the discrimination between normal and abnormal behaviors easier. The MII is mainly based on the optical flow magnitude, and angle difference computed between the optical flow vectors in consecutive frames. A CNN is employed to learn normal and abnormal crowd behaviors using MIIs. The MII generation, and the combination with a CNN is a new approach in the context of abnormal crowd behavior detection. Experiments are performed on commonly used datasets such as UMN and PETS2009. Evaluation indicates that our method achieves the best results.

Subject Keywords

General Engineering, General Materials Science, General Computer Science, Crowd behavior analysis, Anomaly detection, Motion information image, Convolutional neural network

URI

https://hdl.handle.net/11511/52303

Collections

Department of Electrical and Electronics Engineering, Article