# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 4 Experiment
### Evaluation Metrics  
This section introduces the evaluation metrics used to assess the performance of the HP-YOLO model in object detection and human pose estimation tasks. These metrics offer a comprehensive understanding of the model's accuracy, computational efficiency, and real-time performance. OKS [32] is utilized for human pose estimation tasks. It measures the similarity between predicted keypoints and the ground truth keypoints by computing the normalized distance between them. The OKS metric is defined as follows:  
\[\text{OKS}=\frac{\sum_{i}\exp\biggl{(}-\frac{d_{i}^{2}}{2^{8}k_{i}^{2}}\biggr{)} \delta(v_{i}>0)}{\sum_{i}\delta(v_{i}>0)} \tag{9}\]  
where \(d_{i}\) represents the Euclidean distance between the predicted keypoint and the corresponding ground truth keypoint, \(s\) is the object scale, \(k_{i}\) is a keypoint-specific constant adjusting for object size, and \(v_{i}\) is the visibility flag for keypoint \(i\). OKS is similar to IoU but adapted for keypoint detection. Average Precision (AP) is the area under the precision-recall curve, evaluating the detection performance at various recall levels. In this study, we report AP\({}^{50}\) (IoU = 0.50) and AP\({}^{50,95}\), which averages AP over IoU thresholds from 0.50 to 0.95. The mean AP (mAP) is calculated as follows:  
\[\text{mAP}=\frac{1}{n}\sum_{i=1}^{n}\text{AP}^{i} \tag{10}\]  
where \(n\) is the number of object categories, and AP\({}^{i}\) represents the average precision for each category. We also compute AP\({}^{\text{M}}\) and AP\({}^{\text{L}}\), which represent the model's average precision for medium-sized and large-sized objects, respectively:  
* AP\({}^{\text{M}}\): evaluates objects with an area between 32\({}^{2}\) and 96\({}^{2}\) pixels. * AP\({}^{\text{L}}\): evaluates objects with an area greater than 96\({}^{2}\) pixels. GMACs (Giga Multiply-Accumulate Operations) measures the computational complexity of the model by calculating the number of floating-point operations required to process a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Value** & **Notes** \\ \hline Image Size & 640 \(\times\) 640 & Input resolution \\ Batch Size & 8 & SGD optimizer \\ Epochs & 300 & Cosine LR decay \\ Initial LR & 0.0005 & 5-epoch warm-up \\ Weight Decay & 0.0005 & momentum = 0.9 \\ \hline
**Data Augmentations** & & \\ \hline Random Scale & \(\pm\)10\% & \\ Random Rotation & \(\pm\)10\({}^{\circ}\) & \\ HSV (H, S, V) & (0.015, 0.7, 0.4) & Probability = 1.0 each batch \\ Mosaic Probability & 0.4 & Four-image mosaic \\ MixUp Probability & 0.5 & Secondary blending \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training hyperparameters and data augmentation settings. single input image. A lower GMACs value indicates a more computationally efficient model, which is crucial for resource-constrained environments or real-time applications. Latency refers to the average time taken by the model to process a single image during inference. It is an important metric for real-time systems, where lower latency translates to faster processing speeds. Latency is measured in milliseconds (ms). Effective Receptive Field (ERF) quantifies the area of the input image that contributes to a specific output activation in the feature map. Larger ERFs allow the model to capture more global context, improving detection accuracy for larger objects and increasing robustness to occlusions. The ERF is calculated by tracing the model's forward pass and identifying the regions of the input image that influence each output. This method follows the approach from [37], where the HP-YOLO model uses large convolution kernels (e.g., \(31\times 31\)) to expand its ERF without increasing network depth. T - Score (\(\mathrm{\#\%}\)) measures the percentage of the receptive field that effectively contributes to the output prediction. It quantifies how much of the input image's information is utilized by the model. A higher T-Score indicates more efficient use of the receptive field, which leads to more accurate predictions in complex scenes.