Multi-Person Pose Estimation Evaluation Using Optimal Transportation and Improved Pose Matching

2025-07-26

2025 19th International Conference on Machine Vision and Applications (MVA)

AI Summary

Existing evaluation metrics for multi-person pose estimation overly rely on the ranking of high-confidence detections while neglecting low-confidence false positives, leading to biased assessments. To address this limitation, this work proposes OCpose, which introduces optimal transport theory into pose evaluation for the first time. By employing a confidence-weighted matching strategy, OCpose achieves globally optimal alignment between detected poses and ground-truth annotations. This approach abandons the conventional reliance on confidence-based ranking and instead fairly balances true positives against false positives, yielding a more comprehensive and unbiased evaluation of model performance. As a result, OCpose significantly enhances the robustness and reasonableness of pose estimation assessment.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements

Abstract

In Multi-Person Pose Estimation (MPPE), many metrics place importance on ranking of pose detection confidence scores. Current metrics tend to disregard false-positive poses with low confidence, focusing primarily on a larger number of high-confidence poses. Consequently, these metrics may yield high scores even when many false-positive poses with low confidence are detected. For fair evaluation taking into account a tradeoff between true-positive and false-positive poses, this paper proposes Optimal Correction Cost for pose (OCpose), which evaluates detected poses against pose annotations as an optimal transportation. For the fair tradeoff between true-positive and false-positive poses, OCpose equally evaluates all the detected poses regardless of their confidence scores. In OCpose, on the other hand, the confidence score of each pose is utilized to improve the reliability of matching scores between the estimated pose and pose annotations. As a result, OC-pose provides a different perspective assessment than other confidence ranking-based metrics.

Problem

• Multi-Person Pose Estimation
• Evaluation Metrics
• False Positives
• Optimal Transportation
• Pose Matching

Innovation

• Optimal Transportation
• Pose Matching
• Multi-Person Pose Estimation
• Evaluation Metric
• False-Positive Handling