[논문]Multiscale Convolutional Neural Networks for Hand Detection

Yan, Shiyang; Xia, Yizhang; Smith, Jeremy S.; Lu, Wenjin; Zhang, Bailing

doi:10.1155/2017/9830641

Multiscale Convolutional Neural Networks for Hand Detection 원문보기

Applied computational intelligence and soft computing, v.2017, 2017년, pp.1 - 13

Yan, Shiyang (Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China) , Xia, Yizhang (Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China) , Smith, Jeremy S. (Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK) , Lu, Wenjin (Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China) , Zhang, Bailing (Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China)

Abstract ▼ AI-Helper

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.

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Multiscale Convolutional Neural Networks for Hand Detection 원문보기

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