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.

通过分析视频物体检测的时序特征，本文指出平均精度（AP）无法充分反映视频物体检测的实际情况，提出了一种全面的度量准则 —— 平均延迟 (AD)，并从 ImageNet VIDT 数据集上证明了大多数方法虽然能保持平均精度不变，但其检测时延却大幅提高，因此建议特别针对自动驾驶等时延关键应用场景，探索使用延迟度量指标评估视频物体检测方法。