Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (9): 3042-3048.doi: 10.13229/j.cnki.jdxbgxb.20240529

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Adaptive real⁃time gesture classification algorithm based on gesture frame sequence extraction

Lin LIN1,2(),Yu-xin CHEN1,Wei-zhi NAI1,2   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130021，China
    2.Inteligent Signal Identification Equipment Engineering and Technology Research Center of Jilin Province，Jilin University，Changchun 130021，China
  • Received:2024-05-14 Online:2025-09-01 Published:2025-11-14

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Abstract:

In order to improve the processing efficiency of the gesture recognition algorithm for real-time gestures， an adaptive real-time classification algorithm based on gesture frame sequence extraction was proposed. The algorithm extends the frame sequence of the current frame of image data extracted in real-time， completes the classification task with the sequentially extracted multi-frame sequences and performs the joint discriminant， which can recognize static and dynamic gestures at the same time， and obtains the real-time recognition results quickly. The proposed algorithm acccupies less computational resources to shortens the recognition time， solves the delay problem of gesture recognition， and improves the real-time performance. Through the gesture recognition real-time performance experimental test， the proposed algorithm controls the average recognition time of gesture within 0.4 s.

Key words: communications and information systems, real-time gesture recognition, human-computer interaction, frame sequence extraction

CLC Number: 

  • TP391.41

Fig.1

Algorithm diagram for real-time gesture trajectory construction based on random line segments"

Fig.2

Schematic diagram of gesture feature trajectory construction"

Fig.3

Hand signals 0~9"

Fig.4

Gesture recognition rate with threshold coefficient transformation plot"

Fig.5

Schematic diagram of adaptive and non-adaptive algorithms"

Fig.6

Timestamp of the video frame in which the gesture was made and showing gesture recognition results"

Table 1

Ten gesture recognition response schedules"

手势

标签

非自适应算法识别响应时间/s自适应算法识别响应时间/s识别响应时间缩短比率/%
01.330.4069.9
11.380.3773.2
21.370.3375.9
31.400.3773.6
41.510.4768.9
51.500.4967.3
61.200.3075.0
71.340.4367.9
81.350.4070.4
91.210.2381.0
[1] 解迎刚, 王全. 基于视觉的动态手势识别研究综述[J]. 计算机工程与应用, 2021, 57(22): 68-77.
Xie Ying-gang, Wang Quan. Summary of dynamic gesture recognition based on vision[J]. Computer Engineering and Applications, 2021, 57(22): 68-77.
[2] Ahmed S, Cho S H. Hand gesture recognition using an IR-UWB radar with an inception module-based classifier[J]. Sensors, 2020, 20(2): No.564.
[3] Ma M, Chen Z X, Wu J. A Recognition Method of Hand Gesture with CNN-SVM Model[M]. Heidelberg: Springer, 2016.
[4] Tan Y S, Lim K M, Lee C P. Hand gesture recognition via enhanced densely connected convolutional neural network[J]. Expert Systems with Applications, 2021, 175: No.114797.
[5] Witchuda T, Wiranata A, Maeda S,et al. Reservoir computing model for human hand locomotion signal classification[J]. IEEE Access, 2023, 11: 19591-19601.
[6] Yeh S C, Wu E H K, Lee Y R, et al. User experience of virtual-reality interactive interfaces: a comparison between hand gesture recognition and joystick control for XRSPACE MANOVA[J]. Applied Sciences, 2022, 12(23): No.12230.
[7] 赵忠宇, 高博, 龙行锐, 等. 实时FPGA手势识别算法的设计[J]. 电子测量技术, 2020, 43(20): 89-92.
Zhao Zhong-yu, Gao Bo, Long Xing-rui, et al. Design of real-time figure recognition algorithm based FPGA[J]. Electronic Measurement Technology, 2020, 43(20): 89-92.
[8] 杨建华, 李正, 赵妤, 等. 基于肌电信号的嵌入式手势识别系统设计[J]. 自动化与仪表, 2021, 36(12): 62-66.
[9] Li X, Zhao B, Lu X. Key frame extraction in the summary space[J]. IEEE Transactions on Cybernetics, 2017, 48(6): 1-12.
[10] Gharbi H, Bahroun S, Zagrouba E. Key frame extraction for video summarization using local description and repeatability graph clustering[J]. Signal, Image and Video Processing, 2019, 13(3): 507-515.
[11] 邵进达, 杨帅, 程琳. 改进SIFT算法结合两级特征匹配的无人机图像匹配算法[J]. 计算机科学, 2019, 46(6): 316-321.
Shao Jin-da, Yang Shuai, Cheng Lin. UAV image matching algorithm based on improved SIFT algorithm and two-stage feature matching[J]. Computer Science, 2019, 46(6): 316-321.
[12] Nai W Z, Liu Y, Rempel D, et al. Fast hand posture classification using depth features extracted from random line segments[J]. Pattern Recognition, 2017, 65: 1-10.
[13] Nai W, Liu Y, Wang Q, et al. Learning end-user customized mid-air hand gestures using a depth image sensor[J]. IEEE Sensors Journal, 2022, 22(17): 16994-17004.
[14] Maqueda A I, del-Blanco C R, Jaureguizar F, et al.Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns[J]. Computer Vision and Image Understand, 2015, 141: 126-137.
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