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JCS-Net : joint classification and super-resolution network for small-scale pedestrian detection in surveillance images
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Pang, Yanwei, Cao, Jiale, Wang, Jian and Han, Jungong (2019) JCS-Net : joint classification and super-resolution network for small-scale pedestrian detection in surveillance images. IEEE Transactions on Information Forensics and Security, 14 (12). pp. 3322-3331. doi:10.1109/TIFS.2019.2916592 ISSN 1556-6013.
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WRAP-JCS-Net-joint-classification-super-resolution-small-scale-images-Han-2019.pdf - Accepted Version - Requires a PDF viewer. Download (877Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TIFS.2019.2916592
Abstract
While Convolutional Neural Network (CNN)-based pedestrian detection methods have proven to be successful in various applications, detecting small-scale pedestrian from surveillance images is still challenging.The major reason is that the small-scale pedestrians lack much detailed information compared to the large-scale pedestrians. To solve this problem, we propose to utilize the relationship between the large-scale pedestrians and the corresponding small-scale pedestrians to help recover the detailed information of the small-scale pedestrians, thus improving the performance of detecting small-scale pedestrians. Specifically, a unified network (called JCS-Net) is proposed for small-scale pedestrian detection, which integrates the classification task and the super-resolution task in a unified framework. As a result, the super-resolution and classification are fully engaged and the super-resolution sub-network can recover some useful detailed information for the subsequent classification. Based on HOG+LUV and JCS-Net, multi-layer channel features (MCF) are constructed to train the detector. Experimental results on the Caltech pedestrian dataset and the KITTI benchmark demonstrate the effectiveness of the proposed method. To further enhance the detection, multi-scale MCF based on JCS-Net for pedestrian detection is also proposed, which achieves the state-of-the-art performance.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TE Highway engineering. Roads and pavements |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Computer vision , Image processing, Pattern recognition systems, Electronic surveillance., Intelligent transportation systems | ||||||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Information Forensics and Security | ||||||||||||||||||
| Publisher: | IEEE | ||||||||||||||||||
| ISSN: | 1556-6013 | ||||||||||||||||||
| Official Date: | December 2019 | ||||||||||||||||||
| Dates: |
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| Volume: | 14 | ||||||||||||||||||
| Number: | 12 | ||||||||||||||||||
| Page Range: | pp. 3322-3331 | ||||||||||||||||||
| DOI: | 10.1109/TIFS.2019.2916592 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2019 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 | ||||||||||||||||||
| Date of first compliant deposit: | 25 June 2019 | ||||||||||||||||||
| Date of first compliant Open Access: | 25 June 2019 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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