Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline
This work addresses a rarely explored problem in computer vision for detecting amorphous, indistinct objects, which is incremental as it introduces a new dataset and baseline method.
The paper tackles the challenging task of Insubstantial Object Detection (IOD) by constructing a new dataset of 600 videos with 141,017 frames and developing a spatio-temporal aggregation framework that significantly improves performance.
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}.