Grayscale-Thermal Foreground Detection Dataset
Description
It is urgent need to study the multi-model moving object detection due to its own shortness of inadequate of single model videos. However, almost no complete good multi-model datasets to use, thus, we proposed a multi-model moving object detection dataset and the specific details as followings.Our multi-model moving object detection dataset mainly considered 7 challenges, i.e. interminttent motion, low illumination, bad weather, intense shadow, dynamic scene, background clutter, thermal crossover et al.
The following main aspects are taken into account in creating the grayscale-thermal video:
1. Scene category. Including laboratory rooms, campus roads, playgrounds and water pools et al.
2. Object category. Including rigid and non-rigid objects, such as vehicles, pedestrians and animals.
3. Intermittent motion.
4. Shadow effect.
5. Illumination condition.
6. Background factor.
Citation
Chenglong Li, Xiao Wang, Lei Zhang, Jin Tang, Hejun Wu, Liang Lin*, “WELD: Weighted Low-rank Decomposition for Robust Grayscale-Thermal Foreground Detection”, IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), DOI: 10.1109/TCSVT.2016.2556586, 2016.
