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Background Subtraction With Real-Time Semantic Segmentation

Zeng, Dongdong and Chen, Xiang and Zhu, Ming and Goesele, Michael and Kuijper, Arjan (2019):
Background Subtraction With Real-Time Semantic Segmentation.
In: IEEE Access, 7, pp. 153869-153884. ISSN 2169-3536,
DOI: 10.1109/ACCESS.2019.2899348,
[Article]

Abstract

Accurate and fast foreground (FG) object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel BGS framework with the real-time semantic segmentation. Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background (BG) models and segments FG objects. The real-time semantic segmenter S is used to re_ne the FG segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It , the BGS segmenter B computes a preliminary FG/BG mask Bt . At the same time, the real-time semantic segmenter S extracts the object-level semantics St . Then, some speci_c rules are applied on Bt and St to generate the _nal detection Dt . Finally, the re_ned FG/BG mask Dt is fed back to update the BG model. The comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves the state-of-the-art performance among all unsupervised BGS methods while operating at the real-time and even performs better than some deep learning-based supervised algorithms. In addition, our proposed framework is very _exible and has the potential for generalization.

Item Type: Article
Erschienen: 2019
Creators: Zeng, Dongdong and Chen, Xiang and Zhu, Ming and Goesele, Michael and Kuijper, Arjan
Title: Background Subtraction With Real-Time Semantic Segmentation
Language: English
Abstract:

Accurate and fast foreground (FG) object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel BGS framework with the real-time semantic segmentation. Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background (BG) models and segments FG objects. The real-time semantic segmenter S is used to re_ne the FG segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It , the BGS segmenter B computes a preliminary FG/BG mask Bt . At the same time, the real-time semantic segmenter S extracts the object-level semantics St . Then, some speci_c rules are applied on Bt and St to generate the _nal detection Dt . Finally, the re_ned FG/BG mask Dt is fed back to update the BG model. The comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves the state-of-the-art performance among all unsupervised BGS methods while operating at the real-time and even performs better than some deep learning-based supervised algorithms. In addition, our proposed framework is very _exible and has the potential for generalization.

Journal or Publication Title: IEEE Access
Journal volume: 7
Uncontrolled Keywords: Image segmentation Model based segmentations Video segmentation Realtime systems
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Date Deposited: 17 Apr 2020 10:28
DOI: 10.1109/ACCESS.2019.2899348
Official URL: https://ieeexplore.ieee.org/document/8645635
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