SHD360: A Benchmark Dataset for Salient Human Detection in 360° Videos
arXiv:2105.11578
Abstract
Salient human detection (SHD) in dynamic 360° immersive videos is of great importance for various applications such as robotics, inter-human and human-object interaction in augmented reality. However, 360° video SHD has been seldom discussed in the computer vision community due to a lack of datasets with large-scale omnidirectional videos and rich annotations. To this end, we propose SHD360, the first 360° video SHD dataset which contains various real-life daily scenes. Since so far there is no method proposed for 360° image/video SHD, we systematically benchmark 11 representative state-of-the-art salient object detection (SOD) approaches on our SHD360, and explore key issues derived from extensive experimenting results. We hope our proposed dataset and benchmark could serve as a good starting point for advancing human-centric researches towards 360° panoramic data. The dataset is available at https://github.com/PanoAsh/SHD360.
arXiv comment21 pages, 13 figures, 5 tables; Project page: https://github.com/PanoAsh/SHD360; Technical report
References in corpus (5)
- DeepPose: Human Pose Estimation via Deep Neural Networks
- Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
- The EuroCity Persons Dataset: A Novel Benchmark for Object Detection
- Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach
- Human-centric Relation Segmentation: Dataset and Solution