# RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline
## 5 Experiment
### Action Sequence Similarity Identification Based on Frame-Matching Integration  
To validate the effectiveness of our method on dynamic actions, we applied a frame-matching technique to transfer pose-level similarity comparison methods to action matching [48]. Here, we employed the dynamic time warping (DTW) method [49; 50] to match features extracted using the aforementioned RotJoint-based approach. Based on Section 5.2, we utilized the RotJoint-based method with a threshold set at 0.72 to measure the similarity of single-frame images. In this simplified experiment, we did not account for the speed of the action, focusing solely on the sequence of movements and the similarity between poses  
\begin{table}
\begin{tabular}{l c c|c c c c|c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**2D Skeleton**} & \multicolumn{3}{c}{**3D Skeleton**} & \multicolumn{3}{c}{**RotJoints**} \\ \cline{2-10}  & **0.73** & **0.71** & **0.72** & **0.72** & **0.74** & **0.73** & **0.69** & **0.71** & **0.70** \\ \hline Viewpoint & 53.2 & 54.4 & 57.8 & 81.3 & 82.5 & 84.6 & 89.7 & 92.1 & **93.5** \\ Shape & 73.2 & 75.5 & 78.0 & 68.6 & 70.0 & 72.1 & 88.7 & 91.0 & **92.7** \\ Torsional & 72.7 & 74.0 & 75.9 & 74.1 & 75.4 & 77.6 & 88.4 & 90.7 & **91.6** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of 2D skeleton-based, 3D skeleton-based, and RotJoint-based methods on different sets of PoseAMBench. The underlined values indicate the maximum results for each method across different thresholds within a single dataset, while the bolded values represent the absolute maximum values across all methods and settings in the entire study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Axis Angle**} & \multirow{2}{*}{**Quatemion**} & \multicolumn{2}{c}{**Rotation**} & \multirow{2}{*}{**Euler Angle**} \\  & & & & **Matrix** & \\ \hline Method (1) & 84.3 & 85.2 & 87.3 & **92.6** \\ Method (2) & 91.3 & 87.9 & 91.2 & 90.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of different rotation parameter representations in PoseAMBench. The bolded values represent the maximum values across all settings.  
in individual frames. DTW measures the distance between two sequences by calculating the maximum similarity and the formula is  
\[S_{dtw}(X,Y)=\max\left(\sum_{(i,j)\in P}\sin(x_{i},y_{j})\right)\]  
where \(\sin(x_{i},y_{j})\) represents the similarity between pose pairs \(x_{i}\) and \(y_{j}\), and \(P\) is the set of alignment paths. The matching path \(P\) is obtained by selecting the alignment path that maximizes the total similarity:  
\[P=\{(i_{1},j_{1}),(i_{2},j_{2}),\ldots,(i_{k},j_{k})\}\]  
where \(i\) and \(j\) are the indices corresponding to points in sequences \(X\) and \(Y\), respectively, and the path is chosen to maximize similarity. To demonstrate the reliability of the RotJoint-based method in temporal action matching, we conducted tests on videos of the same individual performing the same action at varying speeds, recorded from different viewpoints. Fifty single-person motion videos, each with an average duration of 2 min and a frame rate of 1 fps, were used. The original videos played at 1\(\times\) speed, while the comparison videos were divided into 4-5 segments, with speeds ranging from 0.1\(\times\) to 2\(\times\). The average score achieved was 0.996. In a more complex scenario involving different individuals performing the same action at varying speeds and viewpoints, like Figure 6, we obtained a score of 0.909 using the same setup.  
These results highlight the strong transferability and robustness of the RotJoint-based method for action sequence matching, demonstrating its effectiveness under both simple and complex conditions, including variations in speed and viewpoint. The high average scores in these tests underscore the method's potential for broad application in video-based action comparison tasks, ensuring accurate matching across dynamic conditions.