| Paper ID | IVMSP-11.3 |
| Paper Title |
Cross Scene Video Foreground Segmentation via Co-occurrence Probability Oriented Supervised and Unsupervised Model Interaction |
| Authors |
Dong Liang, Nanjing University of Aeronautics and Astronautics, China; Bin Kang, Nanjing University of Posts and Telecommunications, China; Xinyu Liu, Han Sun, Liyan Zhang, Ningzhong Liu, Nanjing University of Aeronautics and Astronautics, China |
| Session | IVMSP-11: Image & Video Segmentation |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVSMR] Image & Video Sensing, Modeling, and Representation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Using only one deep model for cross scene video foreground segmentation is still very challenging because existing methods are scene-dependent, which restricts the consistent segmentation. In this paper, we propose a cross scene video foreground segmentation framework to extend the supervised model's generalization capability depending on scene-specific training. The proposed framework flexibly utilizes 3 well-trained supervised models as guidance to yield a coarse segmentation mask. The co-occurrence probability-based unsupervised background subtraction model is introduced to achieve scene adaptation in the plug and play style without any fine-tuning and labels. Experimental results on LIMU and CDNet2014 datasets validate our framework outperforms the state-of-the-art supervised/unsupervised approaches that participate in the comparison. Experiments also show the training efficiency-related improvements -- when introducing the guidance models, the demand for quantity and quality of training samples to train the unsupervised model is reduced. Codes: https://github.com/MeteoorLiu/Venus/tree/MeteoorLiu-SUMC |
