## 5 Experiments and Results
### Multi-Person Pose Estimation and Tracking
#### 5.1.3 Results on the PoseTrack Benchmark
\begin{table}
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**Method** & **Dataset** & **AP** & **AP (50)** & **AP (75)** & **AR** & **AR (50)** & **Latency (ms)** \\ \hline RTMO-s [42] & COCO & 67.7 & 87.8 & 73.7 & 71.5 & 90.8 & 18.5 \\ \hline RTMO-m [42] & COCO & 70.9 & 89.1 & 77.8 & 74.7 & 92.1 & 22.6 \\ \hline RTMO-I [42] & COCO & 72.4 & 89.5 & 78.8 & 76.2 & 92.6 & 37.5 \\ \hline RTMO-I [42] & mix & 56.7 & 80.1 & 61.2 & 61.1 & 83.6 & 14.2 \\ \hline RTMO-s [42] & mix & 68.7 & 87.8 & 74.6 & 72.5 & 90.6 & 18.5 \\ \hline RTMO-m [42] & mix & 72.4 & 89.5 & 78.9 & 76.4 & 92.9 & 22.6 \\ \hline RTMO-I [42] & mix & 74.8 & 91.7 & 81.9 & 78.5 & 92.8 & 37.5 \\ \hline YOLO-POSE+ [12] & COCO & 51.9 & 78.3 & 55.6 & 57.1 & 83.5 & 32.2 \\ \hline YOLO-POSE-s t [12] & COCO & 64.4 & 87.2 & 71.2 & 68.1 & 90.5 & 45.6 \\ \hline YOLO-POSE-m t [12] & COCO & 69.5 & 89.7 & 76.4 & 73.7 & 92.6 & 51.3 \\ \hline YOLO-POSE-1 t [12] & COCO & 71.3 & 90.8 & 78.4 & 74.9 & 92.8 & 60.9 \\ \hline debias-ipr-resnet-50 [43] & COCO & 67.5 & 87.2 & 71.4 & 76.5 & 91.4 & 89.5 \\ \hline Simcc-s-vipnas-mobilenetv3 [44] & COCO & 69.8 & 88.2 & 72.7 & 75.6 & 92.7 & 39.5 \\ \hline ours: ResNet-50 & COCO & 76.7 & 92.5 & 83.2 & 79.9 & 94.1 & 59.2 \\ \hline ours: ResNet-50 & mix & 78.5 & 93.1 & 84.9 & 80.2 & 95.1 & 59.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Multi-person pose estimation performance on COCO challenge dataset. \begin{table}
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\begin{table}
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Our method excels not only in analysis accuracy but also in terms of running latency, as demonstrated in Table 6. The table presents a comparison of our method's performance on a single NVIDIA GeForce RTX 3090 by NVIDIA Corporation in Santa Clara, California, USA and sourced from Taiwan for different batch sizes, alongside existing state-of-the-art (SOTA) methods. To ensure fairness in the experiment, we replicated all the methods listed in the table and meticulously aligned the parameter details involved in each method. The \"ST baseline\" refers to the simplest implementation of our method, which involves spatiotemporal sampling of video frames, data fusion for tracking results, and recording the entire inference time of the process. Our method, referred to as the \"ST method\" in the table, efficiently samples and fuses video frames to generate pose estimation and tracking results while fully recording the inference time for this process. We conducted an experiment where we applied the tracking technology of our ST method to Lite Pose, a lightweight 2D attitude estimation network without a complete attitude tracking stage. The results were surprisingly impressive, as the end-to-end process became faster. This improvement was primarily due to the lightweight design in pose estimation, which naturally leads to a strong speedup ratio, ultimately accelerating the inference of the entire pose tracking on videos. Furthermore, we compared our naive implementation work with the latest works such as those presented in CVPR2022 and CVPR2023, specifically in terms of inference speed. In the case of multiple batches, the maximum acceleration ratio reached 1.01-1.24\(\times\), showcasing the efficiency of our method. Overall, our method not only delivers remarkable analysis accuracy but also demonstrates notable improvements in running latency, outperforming existing approaches and achieving impressive acceleration ratios in various scenarios.