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Taking a look at small-scale pedestrians and occluded pedestrians
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Cao, Jiale, Pang, Yanwei, Han, Jungong, Gao, Bolin and Li, Xuelong (2019) Taking a look at small-scale pedestrians and occluded pedestrians. IEEE Transactions on Image Processing, 29 . pp. 3143-3152. doi:10.1109/TIP.2019.2957927
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WRAP-taking-look-small-scale-pedestrians-occluded-pedestrians-Han-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1803Kb) | Preview |
Official URL: https://doi.org/10.1109/TIP.2019.2957927
Abstract
Small-scale pedestrian detection and occluded pedestrian detection are two challenging tasks. However, most state-of-the-art methods merely handle one single task each time, thus giving rise to relatively poor performance when the two tasks, in practice, are required simultaneously. In this paper, it is found that small-scale pedestrian detection and occluded pedestrian detection actually have a common problem, i.e., an inaccurate location problem. Therefore, solving this problem enables to improve the performance of both tasks. To this end, we pay more attention to the predicted bounding box with worse location precision and extract more contextual information around objects, where two modules (i.e., location bootstrap and semantic transition) are proposed. The location bootstrap is used to reweight regression loss, where the loss of the predicted bounding box far from the corresponding ground-truth is upweighted and the loss of the predicted bounding box near the corresponding ground-truth is downweighted. Additionally, the semantic transition adds more contextual information and relieves semantic inconsistency of the skip-layer fusion. Since the location bootstrap is not used at the test stage and the semantic transition is lightweight, the proposed method does not add many extra computational costs during inference. Experiments on the challenging CityPersons and Caltech datasets show that the proposed method outperforms the state-of-the-art methods on the small-scale pedestrians and occluded pedestrians (e.g., 5.20% and 4.73% improvements on the Caltech).
| Item Type: | Journal Article | ||||||
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| Subjects: | H Social Sciences > HE Transportation and Communications T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Image analysis, Image processing -- Digital techniques, Pattern recognition systems, Optical pattern recognition, Human face recognition (Computer science), Biometric identification, Pedestrians | ||||||
| Journal or Publication Title: | IEEE Transactions on Image Processing | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 1057-7149 | ||||||
| Official Date: | 11 December 2019 | ||||||
| Dates: |
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| Volume: | 29 | ||||||
| Page Range: | pp. 3143-3152 | ||||||
| DOI: | 10.1109/TIP.2019.2957927 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
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