2019
DOI: 10.1109/access.2019.2929432
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Part-Aware Region Proposal for Vehicle Detection in High Occlusion Environment

Abstract: Visual-based vehicle detection has been extensively applied for autonomous driving systems and advanced driving assistant systems, however, it faces great challenges as a partial observation regularly happens owing to occlusion from infrastructure or dynamic objects or a limited vision field. This paper presents a two-stage detector based on Faster R-CNN for high occluded vehicle detection, in which we integrate a part-aware region proposal network to sense global and local visual knowledge among different veh… Show more

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Cited by 25 publications
(20 citation statements)
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References 52 publications
(47 reference statements)
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“…For example, the performance in fast and timevariant flow-field environments should be improved. The studies by Li et al 43 , Kwok and Martínez 44 and Zhang Weiwei et al 48 could serve as a reference to resolve the issues that may be encountered in the future.…”
Section: Discussionmentioning
confidence: 99%
“…For example, the performance in fast and timevariant flow-field environments should be improved. The studies by Li et al 43 , Kwok and Martínez 44 and Zhang Weiwei et al 48 could serve as a reference to resolve the issues that may be encountered in the future.…”
Section: Discussionmentioning
confidence: 99%
“…At present, most researches on the recognition of occluded images mainly focus on large scale traditional objects of visible images, such as pedestrian detection [17], faces recognition [18], cars [19] and other occluded objects detection. In [17], a model based on Deformable Part Network (DPN) was proposed, which has the ability of reasoning and can predict the occluded parts.…”
Section: Occlusion Detectionmentioning
confidence: 99%
“…Firstly, the cascade classifier is applied to identify the eyes and mouth of a human face; then, the physical feature relationship between the eyes and mouth of a person and the face is used to identify and detect the entire masked face. In the context of urban autonomous driving system, Only useful for objects of large scales in simple background [1], [3][4][5], [24] Feature fusion+ attention mechanism Extract features of very small objects(2×2 pixels) Only useful for objects in simple background such as sky and sea [2], [6] Density map Detect the small and high density objects Not useful for single object detection [16] Extra information Has the ability of reasoning and predict the occluded parts Detection on traditional visible datasets [17][18][19] Ours (secondary transfer learning + HNEM)…”
Section: Occlusion Detectionmentioning
confidence: 99%
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“…e experimental results showed that the proposed algorithm significantly improved the vehicle detection accuracy at different detection difficulty levels compared to the original YOLOv3 algorithm, especially for the vehicles with severe occlusion. In [25], the authors presented a two-stage detector based on Faster R-CNN for high-occluded vehicle detection. e partaware RPN is proposed to replace the original RPN at the first stage of the Faster R-CNN module, and the part-aware NMS is proposed to refine final results.…”
Section: Theoretical Basismentioning
confidence: 99%