# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 3 Evaluation Metrics and Benchmark Datasets
### Evaluation Metrics  
Compared to image classification, human pose estimation is a structured regression task. Its evaluation requires not only the quantification of the accuracy of individual keypoints but also the assessment of the plausibility of the overall body configuration. A model may localize most keypoints near their ground truth positions; however, if the predicted pose violates human kinematic constraints, it should not be regarded as a reliable solution. Therefore, evaluation standards in pose estimation serve not only as benchmarks for comparing model performance but also as guidelines for algorithm refinement and optimization. In the early stages of pose estimation research, simple error-based metrics, such as End-Point Error (EPE), were commonly used to evaluate model performance [43]. These metrics primarily relied on the Euclidean distance between predicted and ground truth keypoints. However, as application scenarios became more complex, more expressive and robust evaluation metrics were gradually introduced. These advanced metrics consider multiple factors, including localization error, keypoint importance, object scale, and keypoint visibility. These metrics have since become widely adopted as standard benchmarksin the field. Tables 1 and 2 summarize and compare commonly used evaluation metrics, highlighting their key characteristics and applicability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric Name** & **Computation Principle** & **Advantages** & **Limitations** \\ \hline PCP [44] & A body part is considered correct if both predicted endpoints fall within a tolerance distance of the ground truth endpoints. & Captures structural correctness; accounts for limb connectivity. & Sensitive to limb length; ineffective for small parts; rarely used in modern benchmarks. \\ \hline PCK [45] & A keypoint prediction is correct if it lies within \(\alpha\times\) reference length of the ground truth point. & Simple and intuitive; widely adopted in early pose estimation research. & Threshold-dependent; not scale-invariant; ignores keypoint visibility. \\ \hline PCKh [46] & Similar to PCK but uses head segment length as the normalization factor & Better normalization for human scale; standard for the MPI dataset. & Depends on accurate head annotation; not suitable for multi-person scenarios. \\ \hline AUC [47] & Computes the area under the PCK curve across varying thresholds. & Aggregates performance across multiple thresholds; reduces threshold sensitivity. & Less interpretable; inherits limitations of PCK. \\ \hline OKS [48] & Measures keypoint similarity using a Gaussian penalty based on Euclidean distance, normalized by object scale and keypoint-specific constants. & Scale-invariant; considers keypoint visibility and relative importance. & Sensitive to hyperparameters (e.g., \(\sigma\)); requires manual calibration; complex to compute. \\ \hline AP@OKS [48] & Computes average precision across multiple OKS thresholds (0.50–0.95) using confidence-ranked predictions. & Official COCO benchmark; evaluated both localization and detection confidence. & High computational cost; strongly dependent on confidence ranking; penalizes slight deviations. \\ \hline AR@OKS [48] & Computes average recall across a fixed OKS threshold under varying detection limits (e.g., maxDets = 20). & Measures detection completeness; useful in dense or occluded scenes. & Does not reflect precision; prone to false positives; affected by maxDets setting. \\ \hline \multirow{3}{*}{mAP@OKS [48]} & Standard benchmark for multi-person pose estimation; balances precision and recall across scales and occlusion. & Sensitive to confidence calibration; penalizes invisible or slightly off keypoints; computationally intensive. \\ \hline IoU [49] & Ratio of overlap area to union area between predicted and ground truth bounding boxes. & Intuitive geometric measure; standard in object detection. & Inapplicable to keypoint evaluation; insensitive to pose structure or semantics. \\ \hline \hline \end{tabular}
\end{table}