Explore Spatio-Temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline

Kailai Zhou, Yibo Wang, Tao Lv, Yunqian Li, Linsen Chen, Qiu Shen, Xun Cao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 3104-3115

Abstract


We endeavor on a rarely explored task named Insubstan-tial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indistinct boundary; (2) similarity to surroundings; (3) absence in color. Accordingly, it is far more challenging to distinguish insubstantial objects in a single static frame and the collaborative representation of spatial and tempo-ral information is crucial. Thus, we construct an IOD-Video dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges. In addition, we develop a spatio-temporal aggregation framework for IOD, in which differ-ent backbones are deployed and a spatio-temporal aggregation loss (STAloss) is elaborately designed to leverage the consistency along the time axis. Experiments conducted on IOD-Video dataset demonstrate that spatio-temporal aggregation can significantly improve the performance of IOD. We hope our work will attract further researches into this valuable yet challenging task. The code will be available at: https://github.com/CalayZhou/IOD-Video.

Related Material


[pdf]
[bibtex]
@InProceedings{Zhou_2022_CVPR, author = {Zhou, Kailai and Wang, Yibo and Lv, Tao and Li, Yunqian and Chen, Linsen and Shen, Qiu and Cao, Xun}, title = {Explore Spatio-Temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {3104-3115} }