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With the increasing coverage of video surveillance systems in modern society, demand for using artificial intelligence algorithm to replace humans in violent behavior recognition has also become stronger. By moving some channels in the temporal dimension, temporary shift module (TSM) can achieve the performance of three-dimensional convolution neural network (CNN) with the complexity of two-dimensional CNN, and extract the temporal and spatial information at the same time. Our intuition is that too many temporary shift modules may fuse too much action information in each frame, which weakens the capability of CNN on spatiotemporal information extraction. To verify the aforementioned conjecture, we adjusted the network structure based on TSM, proposed partial TSM, selected the optimal model through experiments, and verified the performance of the algorithm on multiple datasets and our expanded datasets. The proposed optimal model not only reduced the memory usage of hardware but also achieved higher accuracy on multiple datasets with 77.3% running time. Meanwhile, we achieved state-of-the-art performance of 91% on RWF-2000 dataset. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]