Affiliation:
1. Department of Computer Science and Software Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2. Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
Abstract
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.
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Civil and Structural Engineering,Computational Mechanics
Cited by
24 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Multimodal Machine Learning for Sign Language Prediction;IFMBE Proceedings;2023-09-14
2. Hand gesture recognition with focus on leap motion: An overview, real world challenges and future directions;Expert Systems with Applications;2023-09
3. Study on HGR by Using Machine Learning;2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE);2023-05-12
4. Analysis of Machine Learning for Recognizing Hand Gestures;2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE);2023-05-12
5. Cybersecurity Assessment Construction of Artificial Intelligence;Advances on Intelligent Computing and Data Science;2023