基于多算法融合的跌倒行为识别

doi:10.3969/j.issn.1000-1158.2022.01.17

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Abstract A fall behavior recognition algorithm based on multi algorithm fusion is proposed. Firstly, Yolov3 tiny detection algorithm is improved to effectively frame the human anchor and extract the target prospect according to the characteristics of human; then alphapose gesture recognition framework is used to identify the key points of human skeleton, and the main joint diagram of human body is obtained; Finally, taking the coordinate information of human joint diagram as input, the spatiotemporal graph convolution neural network is used to detect and identify falls and other actions, which can effectively detect falls in different scenes. The experimental results show that the fusion algorithm can improve the detection effect of fall behavior in different scenes, the detection accuracy can reach 97.4%, and effectively reduce the false detection rate.

Key words： metrology fall action recognition YOLOv3 tiny detection algorithm gesture recognition spatiotemporal graph convolution

Received: 24 November 2020 Published: 06 January 2022

PACS:	TB973
	TB96

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	Articles by authors
	CHENG Shu-hong
	XIE Wen-rui
	ZHANG Dian-fan
	XU Nan

Cite this article:

CHENG Shu-hong,XIE Wen-rui,ZHANG Dian-fan, et al. Fall Action Recognition Based on Computer Vision[J]. Acta Metrologica Sinica, 2022, 43(1): 107-113.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2022.01.17 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2022/V43/I1/107

［1］王雅磊, 刘腊梅, 张振香. 老年人跌倒评估工具的研究进展［J］. 中国老年学杂志, 2019, 39(9): 2294-2298.
Wang Y L, Liu L M, Zhang Z X. Research progress on fall assessment tools for the elderly［J］. Chinese Journal of Gerontology, 2019, 39(9): 2294-2298.
［2］张烨, 周晓晶, 杨晓童. 基于单目视频的人体运动测量系统［J］. 计量学报, 2019, 40(3): 367-372.
Zhang Y, Zhou X J, Yang X T. Human motion measurement system based on Monocular Video［J］. Acta Metrologica Sinica, 2019, 40 (3): 367-372.
［3］ Alarifi A, Alwadain A. Killer heuristic optimized convolution neural network-based fall detection with wearable IoT sensor devices［J］. Measurement, 2021, 167.
［4］ Jang N, Du X. Design of wearable fall detection system based on inertial sensor［J］. Electronics World, 2016(5): 147-149.
［5］ Min W, Cui H, Rao H, et al. Detection of human falls on furniture using scene analysis based on deep learning and activity characteristics［J］. IEEE Access, 2018, 6: 9324-9335.
［6］ Wang S K, Chen L, Zhou Z X, et al. Human fall detection in surveillance video based on PCANet［J］. Multimedia Tools and Applications, 2016, 75(19):11603-11613..
［7］蔡文郁, 郑雪晨, 郭嘉豪, 等. 基于SVM-MultiCNN模型的视觉感知跌倒检测算法［J］. 杭州电子科技大学学报(自然科学版), 2020, 40(5): 59-66.
Cai W Y, Zheng X C, Guo J H, et al. Visual perception fall detection algorithm based on SVM-MultiCNN model［J］. Journal of Hangzhou University of Electronic Science and Technology (Natural Science Edition), 2020, 40(5): 59-66.
［8］ Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector［C］//European Conference on Computer Vision, Amsterdam Netherlands, 2016, 2011:21-37.
［9］ Ji S, Xu W, Yang M, et al. 3D convolutional neural networks for human action recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(1): 221-231.
［10］ Zhi Y, Hui H. A Fall Detection Method Based on Two-Stream Convolutional Neural Network［J］. Journal of Henan Normal University(Natural Science Edition), 2017, 45(3): 96-101.
［11］程淑红,程彦龙. 融合批量再标准化和YOLOv3的手势识别研究［J］. 计量学报, 2021, 42 (1): 29-34.
Cheng S H, Cheng Y L. Hand Gesture Recognition Algorithm Combined with Batch Renormalization and YOLOv3［J］. Acta Metrologica Sinic, 2021, 42 (1): 29-34.
［12］谭芳,穆平安,马忠雪. 基于YOLOv3检测和特征点匹配的多目标跟踪算法［J］. 计量学报, 2021, 42 (2): 157-162.
Tan F, Mu P A, Ma Z X. Multi-target Tracking Algorithm Based on YOLOv3 Detection and Feature Point Matching［J］. Acta Metrologica Sinica, 2021, 42 (2): 157-162.
［13］ Redmon J, Farhadi A. Yolov3: An incremental improvement［C］// Computer Vision and Pattern Recognition. Berlin/Heidelberg, Germany, 2018,1804-2767.
［14］ Hu J, Shen L, Samuel A, et al. Squeeze-and-Excitation Networks. ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8):2011-2023..
［15］程淑红, 张仕军, 赵考鹏. 基于卷积神经网络的生物式水质监测方法［J］. 计量学报, 2019, 40(4): 721-727.
Cheng S H, Zhang S J, Zhao K P. Biological water quality monitoring method based on convolutional neural network［J］. Acta Metrologica Sinica, 2019, 40(4): 721-727.
［16］赵彦涛, 何永强, 贾利颖, 等. 基于时间序列单维卷积神经网络的水泥熟料游离钙软测量方法［J］. 计量学报, 2020, 41(9): 1152-1162.
Zhao Y T, He Y Q, Jia L Y, et al. Soft Measurement Method for Cement Clinker fCaO Based on Time Series Single-dimensional Convolutional Neural Network［J］. Acta Metrologica Sinica, 2020, 41(9): 1152-1162.
［17］ Fang H S, Xie S, Tai Y W, et al. RMPE: Regional Multi-person Pose Estimation［C］//IEEE International Conference on Computer Vision (ICCV). Venice, Italy, 2017.
［18］ Gkioxari G, Hariharan B, Girshick R, et al. Using k-Poselets for Detecting People and Localizing Their Keypoints［C］// Computer Vision & Pattern Recognition. IEEE, Columbus,USA, 2014.
［19］ Yan S, Xiong Y, Lin D. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition［C］// The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). Hilton New Orleans Riverside, New Orleans, Louisiana, USA, 2018.