Journal article
Probability-based framework to fuse temporal consistency and semantic information for background segmentation
IEEE Transactions on Multimedia, Vol.24, pp.740-754
2022
Abstract
The fusion of temporal consistency and semantic information with limited foreground information for background segmentation using deep learning is an underinvestigated problem. In this paper, we explore the relation between temporal consistency and semantic information based on the law of total probability. A highly concise framework is proposed to fuse these two types of information. A theoretical proof is given to show that the proposed framework is more accurate than either the temporal consistency-based model or the semantic information-based model and that each model is a special case of the proposed framework. The proposed framework is a white-box framework that can easily be embedded into a deep neural network as a merging layer. In the proposed model, only a few parameters must be learned, which substantially reduces the need for a large dataset. In addition, these interpretable parameters reflect our understanding of the background and can be applied to a wide range of environments. Extensive evaluations indicate the promising performance of the proposed method. Our code and trained weights for the experiments are available at GitHub.
Details
- Title
- Probability-based framework to fuse temporal consistency and semantic information for background segmentation
- Authors/Creators
- Z. Zeng (Author/Creator)T. Wang (Author/Creator)F. Ma (Author/Creator)L. Zhang (Author/Creator)P. Shen (Author/Creator)S.A.A. Shah (Author/Creator)M. Bennamoun (Author/Creator)
- Publication Details
- IEEE Transactions on Multimedia, Vol.24, pp.740-754
- Publisher
- IEEE
- Identifiers
- 991005543681507891
- Copyright
- © 2022 IEEE
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
68 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- International collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.953 Video Object Tracking
- Web Of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Telecommunications
- ESI research areas
- Computer Science