Efficient Violence Detection Using 3D Convolutional Neural Networks

Abstract

Automatically analyzing violent content in surveillance videos is of profound significance on many applications, ranging from Internet video filtration to public security protection. In this paper, we propose a deep learning model based on 3D convolutional neural networks, without using hand-crafted features or RNN architectures exclusively for encoding temporal information. The improved internal designs adopt compact but effective bottleneck units for learning motion patterns and leverage the DenseNet architecture to promote feature reusing and channel interaction, which is proved to be more capable of capturing spatiotemporal features and requires relatively fewer parameters. The performance of the proposed model is validated on three standard datasets in terms of recognition accuracy compared to other advanced approaches. Meanwhile, supplementary experiments are carried out to evaluate its effectiveness and efficiency. The final results demonstrate the advantages of the proposed model over the state-of-the-art methods in both recognition accuracy and computational efficiency.