Average delay (AD), a new video object detection metric built from clipped per-instance detection delays under false-positive-ratio controls, reveals that DFF, FGFA, and CaTDet preserve mAP while increasing detection delay.
Quickest Moving Object Detection
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abstract
We present a general framework and method for simultaneous detection and segmentation of an object in a video that moves (or comes into view of the camera) at some unknown time in the video. The method is an online approach based on motion segmentation, and it operates under dynamic backgrounds caused by a moving camera or moving nuisances. The goal of the method is to detect and segment the object as soon as it moves. Due to stochastic variability in the video and unreliability of the motion signal, several frames are needed to reliably detect the object. The method is designed to detect and segment with minimum delay subject to a constraint on the false alarm rate. The method is derived as a problem of Quickest Change Detection. Experiments on a dataset show the effectiveness of our method in minimizing detection delay subject to false alarm constraints.
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cs.CV 1years
2019 1verdicts
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A Delay Metric for Video Object Detection: What Average Precision Fails to Tell
Average delay (AD), a new video object detection metric built from clipped per-instance detection delays under false-positive-ratio controls, reveals that DFF, FGFA, and CaTDet preserve mAP while increasing detection delay.