基于深度学习的跌倒行为识别

doi:10.11896/j.issn.1002-137X.2019.09.014

Abstract

Abstract: With the rapid growth of the aging population,fall detection has become a key issue in the medical and health field.Accurately detecting falling events in the monitoring video and giving feedback in real time can effectively reduce injuries even deaths caused by falls in the elderly.In view of the complex scenes in the monitoring video and multiple similar human behaviors,this paper proposed an improved FSSD (Feature Fusion Single Shot Multibox Detector) fall detection method.Firstly,a video frame forming dataset is extracted from different falling video sequences.Then,the training sample set is input into the improved convolutional neural network until the network converges.Finally,the target category and the location of the target in the video are tested according to the optimized network model.The experimental results show that the improved FSSD algorithm can effectively detect the falling or ADL activities of each frame of image and provide real-time feedback.The detection speed is 24fps (GTX1050Ti),which can meet the real-time requirements while ensuring the detection accuracy.Comparing the improved method with the state-of-the-art fall detection methods,the performance of the improved FSSD is better than other algorithms.The detection of fall behavior in video further validates the feasibility and efficiency of the recognition method based on deep learning.

Key words: Action detection, Convolutional neural network, Deep learning, Fall detection, FSSD target detection algorithm

CLC Number:

TP391

MA Lu, PEI Wei, ZHU Yong-ying, WANG Chun-li, WANG Peng-qian. Fall Action Recognition Based on Deep Learning[J].Computer Science, 2019, 46(9): 106-112.

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Fall Action Recognition Based on Deep Learning

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