CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

• URL: http://arxiv.org/abs/2404.18952v1
• Date: Sat, 27 Apr 2024 20:09:40 GMT
• Title: CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention
• Authors: Damith Chamalke Senadeera, Xiaoyun Yang, Dimitrios Kollias, Gregory Slabaugh,
• Abstract summary: We introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. By focusing on both local and globaltemporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets.
• Score: 21.354382437543315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatiotemporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods.

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