Research on human abnormal behavior detection method based on improved slowfast network

The existing methods for detecting abnormal human behavior suffer from the large size of parameters, a lack of capacity to extract spatial-temporal features effectively and also exhibit imbalances between positive and negative samples, as well as between difficult and easy samples. To cope with these problems, this paper improves SlowFast by taking in attention mechanism and changing loss function. Firstly, use grayscale video frame clips as input data on the fast path to reduce GFLOPs effectively. Another improvement involves swapping out the original Non-local modules with ANN modules, enhancing the capability to capture spatial-temporal features while also decreasing the parameter count. Then, use Focal Loss to classify the fused feature map, addressing the issue of imbalance between positive and negative samples, as well as the challenge of classifying difficult and easy samples. The effectiveness and superiority of this method were ultimately verified through the AVA dataset and actual scene videos.