# An Axial Compression Transformer for Efficient Human Pose Estimation
## 4 Results
### Quantitative Results  
The COCO dataset comprises over 200,000 images and 250,000 person instances; each instance is annotated with 17 keypoints. The COCO dataset is divided into train, validation, and test-dev sets, containing 57k, 5k, and 20k images, respectively. We trained our model on the COCO train2017 set and evaluated it on the COCO validation and COCO test-dev sets. A commonly used evaluation metric for the COCO dataset is Average Precision (AP), which is calculated based on Object Keypoint Similarity (OKS). The formula for calculating OKS is as follows:  
\[OKS=\frac{\sum_{i}e^{-\frac{d_{i}^{2}}{2}s_{i}^{2}s(v_{i}>0)}}{\sum_{i}\delta( v_{i}>0)} \tag{6}\]  
In this case, \(d_{i}\) represents the Euclidean distance between the predicted keypoint coordinates and their corresponding ground truth values. \(v_{i}\) indicates whether each keypoint is visible. \(s\) represents the area of the human part of it, and \(k_{i}\) represents the weight assigned to each keypoint.  
The formula for calculating AP is as follows:  
\[AP_{t}=\frac{\sum_{p}\delta(OKS>t)}{\sum_{p}1} \tag{7}\]  
When t = 0.5 and 0.75, AP is represented as \(AP^{50}\) and \(AP^{75}\), while \(AP^{L}\) and \(AP^{M}\) represent evaluation metrics for large-scale and medium-scale objects, respectively. Additionally, we also employ Average Recall (AR) as an evaluation metric.  
We use GFLOPs as the evaluation standard for model computational cost, where FLOP stands for floating-point operations, \(s\) represents seconds, and FLOPs represent the number of floating-point operations per second. Therefore, GFLOPs represent billions of operations per second. We utilize the thop library to output the GFLOPs in the model network.  
As shown in Table 2, we evaluated the performance of ACPose on the mainstream COCO dataset. The results demonstrate that our ACPose model achieves a more significant reduction in computational cost compared to those of the other prevailing models, while maintaining competitive AP scores. Specifically, compared to TokenPose-L/D6, our ACPose-L/D6 reduces the Transformer-related computational cost by 81.2% with only a 0.4% decrease in accuracy; the results show that the original method, TokenPose, generates a large number of matrix operations due to the excessive data volume in the Transformer layers, resulting in high GFLOPs. Our method significantly reduces the data volume through compression operations, thereby substantially lowering the GFLOPs, primarily within the Transformer structure. Furthermore, our ACPose-B achieves a 47.3% reduction in the Transformer-related computational cost, while improving accuracy by 0.1%.  
Compared to TransPose-H-A6, our method exhibits substantial reductions in computational cost and demonstrates superior accuracy. Additionally, as evident from the table, our approach out-performs the ViTPose and SHaRPose methods by reducing the computational cost without compromising the performance.  
The MPII dataset comprises over 25,000 images and 40,000 person instances, each instance annotated with 16 human keypoints. In the MPII dataset, we primarily utilize the Percentage of Correct Keypoints (PCKs) as the evaluation metric, specifically [EMAIL_ADDRESS] as our final experimental result.  
As shown in Table 3, we evaluated our model on the MPII dataset. Compared to the other mainstream methods, our model demonstrates a significant reduction in the computational cost. Compared to the TokenPose [18] method, \(GFLO\)Ps\({}^{T}\) is reduced by 39.0%, with only a 0.2% loss in accuracy. Specifically, ACPose-L/D6 achieves 96.1% and 86.1% prediction accuracy for the shoulder joint (Sho) and wrist joint (Wri), respectively, surpassing the TokenPose-L/D6 method. This indicates that our approach does not compromise overall prediction accuracy, while achieving a well-balanced efficient human pose estimation network in terms of both precision and computational cost.