Research on hybrid information recognition algorithm and quality of golf swing. (July 2018)

• Record Type: Journal Article
• Authors: Li, Jingmei; Tian, Qiao; Zhang, Guoyin; Zheng, Fangyuan; Lv, Chao; Wang, Jiaxiang
• Is Part Of: Computers & electrical engineering. Volume 69(2018)
• Page Start: 907
• Page End: 919
• Publication Date: 2018-07
• Subjects: Hybrid information system -- Golf gesture recognition -- Static image -- Video sequence; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.02.013
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: As is well known, the target recognition algorithm of hybrid information system has intrinsic disadvantages, such as high time complexity, high performance requirements of hardware and complex operations, in this paper, a fast golf gesture recognition algorithm of static image and video sequence is proposed for the field of sports auxiliary training. In static image recognition, a fast multi-scale aggregation channel feature is utilized to extract hybrid information, and the extraction speed can be improved through an approximate calculation method. An improved AdaBoost classifier is adopted to classify the information. On this basis, the aggregation of channel feature detector locates the prominence region of static image, and then scans the generated fractional sequence through the gesture detector as the feature data of golf gesture in the video sequence. Finally, the real-time judgment of feature data is carried out with a linear support vector machine, the rapid identification of golf swing gesture can therefore be obtained. The experimental results show that the recognition speed is over 30 fps and the accuracy is 97% on iPhone5s and later versions, which suggest the validity of algorithm in practical application.