2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-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
SessionIVMSP-11: Image & Video Segmentation
LocationGather.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
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