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

• URL: http://arxiv.org/abs/2206.11459v2
• Date: Fri, 4 Aug 2023 08:43:08 GMT
• Title: Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline
• Authors: Kailai Zhou, Yibo Wang, Tao Lv, Yunqian Li, Linsen Chen, Qiu Shen, Xun Cao
• Abstract summary: We endeavor on a rarely explored task named Insubstantial Object Detection (IOD), which aims to localize the object with following characteristics. 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 atemporal aggregation framework for IOD, in which different backbones are deployed and atemporal aggregation loss (STAloss) is elaborately designed to leverage the consistency along time axis.
• Score: 16.59161777626215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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