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arXiv · 2206.11459

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
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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}.

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Kailai Zhou, Yibo Wang, Tao Lv, Yunqian Li, Linsen Chen, Qiu Shen, Xun Cao. 2022-06-23. Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline. https://arxiv.org/abs/2206.11459

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