## 4 Experiment
### Analysis of Experimental Results  
For the recognition of a single person, this study used the multi-person pose method, whose target-tracking method is a variant of SiamRPN, and in practice the tracking point was more fixed relative to the target, but the size of the bounding box from which it obtained the target was not constant and sometimes. As shown in Figure 6 below, the tracking was not able to keep up due to the larger movements. Figure 7 illustrates the impact of bounding box size on pose-based motion recognition. Specifically, in the normal situation shown in Figure 7a, all movement joints were included in the bounding box. However, during running, passing, and throwing, as shown in Figure 7b-d, the tracker might fail to include all joints, thereby compromising the accuracy of pose-based motion recognition. To address this issue, this paper proposes a fixed bounding box size of 256 * 192 centered on TrackPoint, which reduces image size variation relative to network input size, enhances model input efficiency, and speeds up pose estimation. When there are multiple people, a multiple person approach is used, which avoids the significant impact of using a single person estimation method. However, this approach raises the problem of multiple people wrapping up, which requires identifying the target being tracked. To address this issue, the target is identified as the person in the centre of the box with the fullest number of joints and the largest relative response size, and the response values are accumulated for the heatmap position corresponding to each person's joints in the box. This approach enhances the accuracy and reliability of pose-based motion recognition. Figure 6: Single-tracking detection frame that does not contain some key points. As shown in Figure 8 below, the location of the Tracking Framework is obtained, the centre point is selected as the location of the target, fed into the gesture estimation network for estimation, and the nodal data is output. In addition, for the later screen convolution action recognition, in order to make gesture estimation out of 200 frames the network runs first. In addition, the nodal point results are entered in the loop array (the loop array length is the space required for 200 frames). When the loop count is detected group is full of 200 frames, the system will take 200 frames of data from the latest video frames obtained from the loop array, and then reverse the sequence.The video frames are entered into the input cache of the network and inferred by the graph convolutional neural network, which eventually starts the graph convolutional network as a category. The new video frames are then overwritten with the oldest frames in the recurrent array, and then 200 frames are taken from the most recent video. Then the video frames are inverted and sent to the convolutional network for inference, and this is repeated until the video ends or tracking fails. Figure 9 shows the application effect of the improved OpenPose model in a realistic environment. The prediction index of the improved lightweight OpenPose model decreases slightly compared with the original model, by about 3. However, the \(AP^{L}\) value of the lightweight model is not much different from the original model when detecting a larger size human body. This is because in this paper, the large size \(7\times 7\) convolutional kernel is replaced by a small size \(3\times 3\) convolutional kernel, and the small convolutional kernel can map the larger size object more nonlinearly in the feature extraction process, so it performs better in this aspect. Figure 8: Overall detection of the tracking frame. Figure 7: Detection of tracking frame monoblocks. In this paper, we compare the experimental results of GNNet with other advanced human pose estimation networks, and Table 2 shows the validation results of GNNet with other advanced networks on the COCO 2017 validation set.