提出新评估方法OCpose，公平衡量多人姿态估计中的真阳与假阳
Multi-Person Pose Estimation Evaluation Using Optimal Transportation and Improved Pose Matching
- 基于最优传输理论，统一评估所有检测姿态，不偏袒高置信度结果
- 通过置信度优化匹配得分，提升真阳匹配可靠性
- 适合关注检测质量平衡的算法开发者与评测人员
在多人姿态估计中，现有评估指标多依赖姿态检测置信度排序，常忽略低置信度的假阳性检测，导致高分但存在大量误检。为此，本文提出一种基于最优传输的姿态评估方法——OCpose，将检测姿态与标注姿态视为运输问题进行最优匹配。该方法对所有检测姿态一视同仁，不因置信度高低而区别对待，从而实现真阳性与假阳性之间的公平权衡。同时，利用姿态置信度改进检测姿态与标注之间的匹配得分可靠性。实验表明，OCpose提供了不同于传统置信度排名指标的新评估视角。
原文摘要 · Abstract (English)
In Multi-Person Pose Estimation, 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, OCpose provides a different perspective assessment than other confidence ranking-based metrics.
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