Human posture recognition based on multiple features and rule learning
- PDF / 1,938,343 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 95 Downloads / 269 Views
ORIGINAL ARTICLE
Human posture recognition based on multiple features and rule learning Weili Ding1 · Bo Hu1 · Han Liu2 · Xinming Wang1 · Xiangsheng Huang3 Received: 17 June 2019 / Accepted: 2 May 2020 © The Author(s) 2020
Abstract The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs. Keywords Human posture recognition · Multiple features · Rule learning
1 Introduction
* Han Liu [email protected] * Xinming Wang [email protected] Weili Ding [email protected] Bo Hu [email protected] Xiangsheng Huang [email protected] 1
Department of Automation, Institute of Electrical Engineering, Key Laboratory of Intelligent Rehabilitation and Neromodulation of Hebei Province, Yanshan University, 438 West of Hebei Avenue, Haigang District, Qinghuangdao 066004, China
2
School of Computer Science and Informatics, Cardiff University, Queen’s Buildings, 5 The Parade, Cardiff CF24 3AA, UK
3
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
In recent years, the use of skeleton data for human posture recognition has emerged as a popular research topic in the computer vision field. This technology shows good prospects for application in human-computer interaction, rehabilitation medicine, multimedia applications, virtual reality, robot control, and others. In general, postures are different from actions, with the former being static and the latter dynamic. A human posture is a base of actions, and is often taken as the key frame in various action recognition algorithms. Moreover, in some fields, such as physical training, rehabilitation training [8] and sign language communication, a human posture is more important than an action. In noisy workshops and dangerous working environments, posture recognition, as a human-computer interaction mode, is much superior to keystroke control and voice interaction in that it is more accurate, efficient and more natural in interaction. There are several main methods for posture rec
Data Loading...
