视频目标检测的延迟度量：平均精度不能说明什么
A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
摘要：
平均精度（AP）是一个广泛使用的度量来评估图像和视频目标检测器的检测精度。在本文中，我们分析了目标检测从视频，并指出，AP单独是不足以捕捉的时间性质的视频目标检测。为了解决这个问题，我们提出了一个全面的度量，平均延迟（AD），测量和比较检测延迟。为了便于延迟评估，我们仔细选择了ImageNet VID的一个子集，我们将其命名为ImageNet VIDT，重点是复杂的轨迹。通过广泛评估各种检测器的VIDT，我们表明，大多数方法大大增加了检测延迟，但仍然保持AP很好。换句话说，AP不够灵敏以反映视频对象检测器的时间特性。我们的研究结果表明，视频目标检测方法应额外评估延迟度量，特别是对于延迟关键型应用，如自主车辆感知。
英文摘要：
Average precision (AP) is a widely used metric to evaluate detection accuracy of image and video object detectors. In this paper, we analyze object detection from videos and point out that AP alone is not sufficient to capture the temporal nature of video object detection. To tackle this problem, we propose a comprehensive metric, average delay (AD), to measure and compare detection delay. To facilitate delay evaluation, we carefully select a subset of ImageNet VID, which we name as ImageNet VIDT with an emphasis on complex trajectories. By extensively evaluating a wide range of detectors on VIDT, we show that most methods drastically increase the detection delay but still preserve AP well. In other words, AP is not sensitive enough to reflect the temporal characteristics of a video object detector. Our results suggest that video object detection methods should be additionally evaluated with a delay metric, particularly for latency-critical applications such as autonomous vehicle perception.
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