Multiscale Convolutional Neural Networks for Hand Detection
Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for dec...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2017/9830641 |
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| _version_ | 1849309028531830784 |
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| author | Shiyang Yan Yizhang Xia Jeremy S. Smith Wenjin Lu Bailing Zhang |
| author_facet | Shiyang Yan Yizhang Xia Jeremy S. Smith Wenjin Lu Bailing Zhang |
| author_sort | Shiyang Yan |
| collection | DOAJ |
| description | Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN) model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge. |
| format | Article |
| id | doaj-art-05e40578b8fa40ddab6bbc2dc8a4e7a9 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-05e40578b8fa40ddab6bbc2dc8a4e7a92025-08-20T03:54:16ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322017-01-01201710.1155/2017/98306419830641Multiscale Convolutional Neural Networks for Hand DetectionShiyang Yan0Yizhang Xia1Jeremy S. Smith2Wenjin Lu3Bailing Zhang4Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UKDepartment of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaDepartment of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaUnconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs) in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN) model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.http://dx.doi.org/10.1155/2017/9830641 |
| spellingShingle | Shiyang Yan Yizhang Xia Jeremy S. Smith Wenjin Lu Bailing Zhang Multiscale Convolutional Neural Networks for Hand Detection Applied Computational Intelligence and Soft Computing |
| title | Multiscale Convolutional Neural Networks for Hand Detection |
| title_full | Multiscale Convolutional Neural Networks for Hand Detection |
| title_fullStr | Multiscale Convolutional Neural Networks for Hand Detection |
| title_full_unstemmed | Multiscale Convolutional Neural Networks for Hand Detection |
| title_short | Multiscale Convolutional Neural Networks for Hand Detection |
| title_sort | multiscale convolutional neural networks for hand detection |
| url | http://dx.doi.org/10.1155/2017/9830641 |
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