Detecting the Background-Similar Objects in Complex Transportation Scenes Article Swipe
Bangyong Sun
,
Ming Ma
,
Nianzeng Yuan
,
Junhuai Li
,
Tao Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tits.2023.3268378
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/tits.2023.3268378
With the development of intelligent transportation systems, most human objects can be accurately detected in normal road scenes. However, the detection accuracy usually decreases sharply when the pedestrians are merged into the background with very similar colors or textures. In this paper, a camouflaged object detection method is proposed to detect the pedestrians or vehicles from the highly similar background. Specifically, we design a guide-learning-based multi-scale detection network (GLNet) to distinguish the weak semantic distinction between the pedestrian and its similar background, and output an accurate segmentation map to the autonomous driving system. The proposed GLNet mainly consists of a backbone network for basic feature extraction, a guide-learning module (GLM) to generate the principal prediction map, and a multi-scale feature enhancement module (MFEM) for prediction map refinement. Based on the guide learning and coarse-to-fine strategy, the final prediction map can be obtained with the proposed GLNet which precisely describes the position and contour information of the pedestrians or vehicles. Extensive experiments on four benchmark datasets, e.g., CHAMELEON, CAMO, COD10K, and NC4K, demonstrate the superiority of the proposed GLNet compared with several existing state-of-the-art methods.
Related Topics
Concepts
Artificial intelligence
Pedestrian detection
Computer science
Benchmark (surveying)
Object detection
Feature extraction
Segmentation
Feature (linguistics)
Computer vision
Pedestrian
Pattern recognition (psychology)
Intelligent transportation system
Scale (ratio)
Position (finance)
Geography
Engineering
Cartography
Linguistics
Archaeology
Philosophy
Finance
Civil engineering
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tits.2023.3268378
- OA Status
- green
- Cited By
- 7
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367146759
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4367146759Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tits.2023.3268378Digital Object Identifier
- Title
-
Detecting the Background-Similar Objects in Complex Transportation ScenesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-26Full publication date if available
- Authors
-
Bangyong Sun, Ming Ma, Nianzeng Yuan, Junhuai Li, Tao YuList of authors in order
- Landing page
-
https://doi.org/10.1109/tits.2023.3268378Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://ir.opt.ac.cn/handle/181661/96463Direct OA link when available
- Concepts
-
Artificial intelligence, Pedestrian detection, Computer science, Benchmark (surveying), Object detection, Feature extraction, Segmentation, Feature (linguistics), Computer vision, Pedestrian, Pattern recognition (psychology), Intelligent transportation system, Scale (ratio), Position (finance), Geography, Engineering, Cartography, Linguistics, Archaeology, Philosophy, Finance, Civil engineering, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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