Computer Science ›› 2019, Vol. 46 ›› Issue (9): 106-112.doi: 10.11896/j.issn.1002-137X.2019.09.014

• NDBC 2018 • Previous Articles     Next Articles

Fall Action Recognition Based on Deep Learning

MA Lu1, PEI Wei2, ZHU Yong-ying3, WANG Chun-li1, WANG Peng-qian1   

  1. (College of Information Science and Technology,Dalian Maritime University,Dalian,Liaoning 116026,China)1;
    (College of Environmental Science and Engineering,Dalian Maritime University,Dalian,Liaoning 116026,China)2;
    (Ocean and Civil Engineering Department,Dalian Ocean University,Dalian,Liaoning 116026,China)3
  • Received:2018-07-09 Online:2019-09-15 Published:2019-09-02

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

CLC Number: 

  • TP391
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