Efficient Anomaly Detection in Crowd Videos Using Pre-Trained 2D Convolutional Neural Networks

Surveillance of crowded places can benefit from improved techniques of anomaly detection in crowd videos. Several existing methods have detected various types of crowd abnormal behaviors by using spatial and temporal information got from videos. So far as real-time detection of anomalies is concerned, special attention must be given to reducing the model complexity that leads to computational and memory loads. This paper proposes a low computational cost approach to detect crowd anomalies. The proposed approach avoids the expensive optical flow calculations by adopting a pre-trained 2D convolutional neural network (CNN) for motion information and implements a lighter form of the 2D CNN to achieve high recognition accuracy at low computational cost. Experiments on public datasets show that the proposed model outperforms the existing approaches in terms of detection accuracy alongside providing better performance in generating input frames.