This study proposes a new method to evaluate the performance of algorithms for moving object detection (MODA) in video sequences. The proposed method is based on human performance metric intervals, instead of ideal metric values (0 or 1) which are commonly used in the literature. These intervals are proposed to establish a more reliable evaluation and comparison, and to identify areas of improvement in the evaluation of MODA. The contribution of the study includes the determination of human segmentation performance metric intervals and their comparison with state-of-the-art MODA, and the evaluation of their segmentation results in a tracking task to establish the impact between performance and practical utility. Results show that human participants had difficulty with achieving a perfect segmentation score. Deep learning algorithms achieved performance above the human average, while other techniques achieved a performance between 88 and 92%. Furthermore, the authors demonstrate that algorithms not ranked at the top of the quantitative metrics worked satisfactorily in a tracking experiment; and therefore, should not be discarded for real applications.

中文翻译：

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目标运动检测算法的类人评价方法

这项研究提出了一种评估视频序列中运动目标检测（MODA）算法性能的新方法。所提出的方法基于人类绩效指标间隔，而不是文献中通常使用的理想指标值（0或1）。建议使用这些间隔来建立更可靠的评估和比较，并确定MODA评估中的改进领域。该研究的贡献包括确定人类分割性能指标间隔并将其与最新的MODA进行比较，评估它们的分割结果将导致跟踪任务，以建立性能和实际效用之间的影响。结果表明，人类参与者难以获得完美的分割分数。深度学习算法的性能高于人类平均水平，而其他技术的性能则达到88％至92％。此外，作者证明，在跟踪实验中，未排在量化指标顶部的算法可以令人满意地工作。因此，在实际应用中不应将其丢弃。