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State-of-the-art object detectors and trackers are developing fast. Trackersare in general more efficient than detectors but bear the risk of drifting. Aquestion is hence raised -- how to improve the accuracy of video objectdetection/tracking by utilizing the existing detectors and trackers within agiven time budget? A baseline is frame skipping -- detecting every N-th framesand tracking for the frames in between. This baseline, however, is suboptimalsince the detection frequency should depend on the tracking quality. To thisend, we propose a scheduler network, which determines to detect or track at acertain frame, as a generalization of Siamese trackers. Although beinglight-weight and simple in structure, the scheduler network is more effectivethan the frame skipping baselines and flow-based approaches, as validated onImageNet VID dataset in video object detection/tracking.

Detect or Track: Towards Cost-Effective Video Object Detection/Tracking