@inproceedings{luo2019detect,
author = {Luo, Hao and Xie, Wenxuan and Wang, Xinggang and Zeng, Wenjun},
title = {Detect or Track: Towards Cost-Effective Video Object Detection/Tracking},
booktitle = {AAAI},
year = {2019},
month = {January},
abstract = {State-of-the-art object detectors and trackers are developing fast. Trackers are in general more efficient than detectors but bear the risk of drifting. A question is hence raised – how to improve the accuracy of video object detection/tracking by utilizing the existing detectors and trackers within a given time budget? A baseline is frame skipping – detecting every N-th frames and tracking for the frames in between. This baseline, however, is suboptimal since the detection frequency should depend on the tracking quality. To this end, we propose a scheduler network, which determines to detect or track at a certain frame, as a generalization of Siamese trackers. Although being light-weight and simple in structure, the scheduler network is more effective than the frame skipping baselines and flow-based approaches, as validated on ImageNet VID dataset in video object detection/tracking.},
url = {http://approjects.co.za/?big=en-us/research/publication/detect-or-track-towards-cost-effective-video-object-detection-tracking/},
}