Violence behavior recognition is an important research scenario in behavior recognition and has broad application prospects in the field of network information review and intelligent security. Inspired by the long-short-term memory network, we estimate that temporal shift module (TSM) may have more room for improvement in the feature extraction ability of long-term information. In order to verify the above conjecture, we explored based on TSM. After many attempts, it was finally proposed to connect the two TSMs in a cascaded manner, which can expand the receptive field of the model. In addition, an efficient channel attention module was introduced at the front end of the network, which strengthened the model’s spatial feature extraction capabilities. At the same time due to behavior recognition prone to over-fitting, we extended and processed on the basis of some open-source datasets to form a larger violence dataset and solved the problem of over-fitting. The final experimental results show that the algorithm proposed can improve the model’s feature extraction ability of violent behavior in the space and temporal dimension and realize the recognition of violent behavior, which verified the above point of view.

中文翻译：

---

具有注意力机制的两级时移模块的暴力行为识别

暴力行为识别是行为识别的重要研究场景，在网络信息审查和智能安全领域具有广阔的应用前景。受长短期记忆网络的启发，我们估计时间移位模块（TSM）可能在长期信息的特征提取能力上有更大的提升空间。为了验证上述猜想，我们基于TSM进行了探索。经过多次尝试，最终提出将两个TSMs级联的方式连接起来，可以扩大模型的感受野。此外，在网络前端引入了高效的通道注意力模块，加强了模型的空间特征提取能力。同时由于行为识别容易出现过拟合，我们在一些开源数据集的基础上进行了扩展和处理，形成了一个更大的暴力数据集，解决了过拟合的问题。最终的实验结果表明，所提出的算法能够提高模型在空间和时间维度上对暴力行为的特征提取能力，实现对暴力行为的识别，验证了上述观点。