# AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
## 4 High Availability of Animal Behavior Recognition
In addition, YOLO-Pose does not require the post-processing step of the underlying method to group the detected key points into skeletons, as each bounding box has an associated pose, which enables the natural grouping of key points. YOLO-Pose achieved new optimal results on the COCO validation set and the test set (90.2% AP50 and 90.3% AP50) and outperform all existing key points in a single forward propagation process, outperforming all existing bottom-up methods without the need for flip tests, multi-scale tests, or other test time enhancements. While the original YOLO pose implements single-shot pose estimation based on the YOLOv5 target detection framework, the platform extends it based on the better-performing YOLOX framework. It provides network structures with different parameter scales such as YOLOX-tiny-Pose, YOLOX-s-Pose, YOLOX-m Pose, YOLOX-s-Pose, YOLOX-m-Pose, and YOLOX-l-Pose. The network structures with different parameter scales can meet the performance constraints of different hardware resources. Figure 3: DeepLabCut network architecture, where EfficientNet is the replaceable feature extraction network. Behavior recognition based on skeleton keypoints is usually done in two ways: one is based on keypoint coordinate information by manual design of matching rules for behavioral categories, such as linear motion behaviors that can be matched by the linear change of the coordinates of the center point of the animal's body between consecutive frames; the other method based on deep learning by the model autonomously learns the keypoint change characteristics of different behavioral categories. The platform provides a behavior recognition algorithm plug-in based on skeleton key points, which selects the ST-GCN network as the underlying algorithm, as shown in Figure 4. For the first time, this network combines the graph convolution operation, which captures spatial dimensional information, and the temporal convolution operation, which captures temporal dimensional information, to form spatiotemporal convolution modules. These modules can extract high-level features of skeleton graph sequences through multiple layers. The ST-GCN network mainly consists of nine layers of basic modules, with the output channels of the first three layers being 64, the middle three layers being 128, and the output channels of the last three layers being 256. In addition, the size of the temporal convolution kernel of each layer is 9. To reduce the feature loss of the network during feature extraction and to improve the feature extraction capability of the model, residual concatenation is used in each base unit to realize the cross-region feature fusion. Meanwhile, to avoid overfitting during the training process and improve the robustness of the model, a dropout layer is added to each base unit. After these processes, the feature vectors generated from the skeleton sequences will finally be fed into the SoftMax classifier for behavioral action classification. Another behavior recognition algorithm plug-in provided by the platform is the SlowFast algorithm based on optical flow features. The algorithm uses hand-designed optical flow features to characterize the movement information of the target between two frames. SlowFast is biologically inspired by a two-pathway structural model, Slow Pathway and Fast Pathway, concerning the characteristics of P-cells, which are used to capture spatial information, and M-cells, which are used to capture fast-moving information, in the retinal cells of primates. Slow Pathway is used to capture spatial semantic information reflected by sparse frames, and it uses a very low frame frequency; Fast Pathway is used to capture rapidly changing running information, and it uses a very high frame frequency. In addition, Slow Pathway has a larger model volume, like 80% P-cells; Fast Pathway is lightweight, like 20% M-cells. In the middle of the two pathways is a Fast to Slow passthrough connection, i.e., the fusion of motion information to spatial semantics.