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.

本文提出了 CUE-Net，一种用于视频监控中自动化暴力检测的新型架构，并通过将空间裁剪与 UniformerV2 架构的增强版本相结合，集成了卷积和自注意机制以及一种新颖的修改的高效加性注意机制（减少了自注意力的二次时间复杂度），从而有效而高效地识别暴力活动。该方法旨在解决传统挑战，如捕捉视频帧中远距离或部分遮挡的对象。通过关注局部和全局的时空特征，CUE-Net 在 RWF-2000 和 RLVS 数据集上实现了最先进的性能，超越了现有的方法。