Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection
Pedestrian detection is a great challenge, especially in complex and diverse occlusion environments. When a pedestrian is in an occlusion situation, the pedestrian visible part becomes incomplete, and the body bounding box contains part of the pedestrian, other objects and backgrounds. Based on this...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/6/966 |
| _version_ | 1797446689725874176 |
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| author | Yu Zhang Hui Wang Yizhuo Liu Mao Lu |
| author_facet | Yu Zhang Hui Wang Yizhuo Liu Mao Lu |
| author_sort | Yu Zhang |
| collection | DOAJ |
| description | Pedestrian detection is a great challenge, especially in complex and diverse occlusion environments. When a pedestrian is in an occlusion situation, the pedestrian visible part becomes incomplete, and the body bounding box contains part of the pedestrian, other objects and backgrounds. Based on this, we attempt different methods to help the detector learn more features of the pedestrian under different occlusion situations. First, we propose region resolution learning, which learns the pedestrian regions on the input image. Second, we propose fine-grained segmentation learning to learn the outline and shape of different parts of pedestrians. We propose an anchor-free approach that combines a pedestrian detector CSP, region Resolution learning and Segmentation learning (CSPRS). We help the detector to learn extra features. CSPRS provides another way to perceive pixels, outline and shapes in pedestrian areas. This detector includes region resolution learning, and segmentation learning helps the detector to locate pedestrians. By simply adding the region resolution learning branch and segmentation branch, CSPRS achieves good results. The experimental results show that both methods of learning pedestrian features improve performance. We evaluate our proposed detector CSPRS on the CityPersons benchmark, and the experiments show that CSPRS achieved 42.53% on the heavy subset on the CityPersons dataset. |
| first_indexed | 2024-03-09T13:45:10Z |
| format | Article |
| id | doaj.art-6662cbb035d145d2be6f8edcf596dfca |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T13:45:10Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-6662cbb035d145d2be6f8edcf596dfca2023-11-30T21:01:44ZengMDPI AGElectronics2079-92922022-03-0111696610.3390/electronics11060966Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian DetectionYu Zhang0Hui Wang1Yizhuo Liu2Mao Lu3The College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, ChinaThe College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, ChinaThe College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, ChinaThe College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, ChinaPedestrian detection is a great challenge, especially in complex and diverse occlusion environments. When a pedestrian is in an occlusion situation, the pedestrian visible part becomes incomplete, and the body bounding box contains part of the pedestrian, other objects and backgrounds. Based on this, we attempt different methods to help the detector learn more features of the pedestrian under different occlusion situations. First, we propose region resolution learning, which learns the pedestrian regions on the input image. Second, we propose fine-grained segmentation learning to learn the outline and shape of different parts of pedestrians. We propose an anchor-free approach that combines a pedestrian detector CSP, region Resolution learning and Segmentation learning (CSPRS). We help the detector to learn extra features. CSPRS provides another way to perceive pixels, outline and shapes in pedestrian areas. This detector includes region resolution learning, and segmentation learning helps the detector to locate pedestrians. By simply adding the region resolution learning branch and segmentation branch, CSPRS achieves good results. The experimental results show that both methods of learning pedestrian features improve performance. We evaluate our proposed detector CSPRS on the CityPersons benchmark, and the experiments show that CSPRS achieved 42.53% on the heavy subset on the CityPersons dataset.https://www.mdpi.com/2079-9292/11/6/966pedestrian detectionresolution learningsegmentation learningcomputer visiondeep learning |
| spellingShingle | Yu Zhang Hui Wang Yizhuo Liu Mao Lu Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection Electronics pedestrian detection resolution learning segmentation learning computer vision deep learning |
| title | Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection |
| title_full | Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection |
| title_fullStr | Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection |
| title_full_unstemmed | Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection |
| title_short | Region Resolution Learning and Region Segmentation Learning with Overall and Body Part Perception for Pedestrian Detection |
| title_sort | region resolution learning and region segmentation learning with overall and body part perception for pedestrian detection |
| topic | pedestrian detection resolution learning segmentation learning computer vision deep learning |
| url | https://www.mdpi.com/2079-9292/11/6/966 |
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