Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline
Abstract
We endeavor on a rarely explored task named Insubstantial 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 temporal 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 different 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: \url{https://github.com/CalayZhou/IOD-Video}.
Cite
@article{arxiv.2206.11459,
title = {Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline},
author = {Kailai Zhou and Yibo Wang and Tao Lv and Yunqian Li and Linsen Chen and Qiu Shen and Xun Cao},
journal= {arXiv preprint arXiv:2206.11459},
year = {2023}
}