## 3 The Proposed Approach
### Improved TSM for Behavior Recognition
The reason for this is that there may be some character movements in the original video that overlap with the background. For instance, the driving action and the wheel usually overlap, so you will miss vital details if you simply clip and fit the person's image. As a result, we intercept the extended target image, fill it with grayscale, crop it randomly, and then use the model to make inferences. The core of the model is the feature extraction network, which has a significant influence on recognition performance. Because of its distinct residual structure, the ResNet series feature extraction network typically improves the model's performance as the number of stacking layers rises. Nevertheless, the model's size directly affects how difficult it is to deploy. Before heedlessly expanding the model size to improve the effect, it is imperative to keep the balance between the scale and the effect. The model performance can be impacted by the width, depth, and resolution of the feature extraction network. EfficientNet involves a new scaling method that uniformly scales all the dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. Google provided eight combination structures with progressively larger scales, ranging from EfficientNet-B0 to EfficientNet-B7. The structures Efficientnet-B0 through to Efficientnet-B7 have much fewer parameters and calculations than the ResNet series of feature extraction networks. Furthermore, EfficientNet models outperformed ResNet networks based on public datasets of comparable size. This research compares the performance of different feature extraction networks based on the self-constructed SCA dataset, and the results are shown in Figure 3. The horizontal axis reflects the computational complexity of the backbone network, while the Top-1 accuracy of the behavior identification is represented by the vertical axis. EfficientNet-B3 is easily found to have the best effect, as a result of the comparison. It achieves great recognition accuracy, with a significant reduction in computational complexity and the number of parameters involved, as compared to the ResNet series of networks. As a result, the feature extraction network used in this study is EfficientNet-B3. Figure 3: The performance of different backbone networks based on the SCA dataset. GMAC (Giga Multiply–Accumulate Operations) serves as a metric for quantifying the computational complexity, while Top-1 Accuracy represents the percentage of instances when the model’s primary (highest probability) prediction correctly matches the actual label. The size of the feature map keeps getting smaller, the number of channels keeps growing, and the features of the model extracts get more abstract, as the feature extraction network gets deeper. As a result, a lot of the features are redundant and are not useful for classification. Thus, we insert a feature improvement layer before the completely connected layer. It primarily employs a CA module to improve the network's representation of useful information, while weakening irrelevant information for classification. The coordinate information embeds positional information. Specifically, it exploits two 1D global pooling operations to aggregate the input features, along the vertical and horizontal directions, respectively, into two separate direction-aware feature maps. These two feature maps with embedded direction-specific information are then separately encoded into two attention maps, each of which captures the long-range dependencies of the input feature map along one spatial direction. The positional information can thus be preserved in the generated attention maps. Both attention maps are then applied to the input feature map via multiplication to emphasize the representations of interest.