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
[1]PENG Y A.A Technical Guide to Falling Interventions for the Elderly[J].Journal of Practical Rural Doctors,2012,19(8):1-13.(in Chinese)彭宇案.老年人跌倒干预技术指南[J].中国实用乡村医生杂志,2012,19(8):1-13.
[2]SANNINO G,FALCO I DE,PIETRO G DE.A supervised approach to automatically extract a set of rules to support fall detection in an mHealth system[J].Applied Soft Computing,2015,34(C):205-216.
[3]National Bureau of Statistics of the People’s Republic of China. Statistical Communique of the 2017 National Economic and Social Development of the People’s Republic of China[J].China Statistics,2018(3):7-20.(in Chinese)中国人民共和国国家统计局.中华人民共和国2017年国民经济和社会发展统计公报[J].中国统计,2018(3):7-20.
[4]NOURY N,FLEURY A,RUMEAU P,et al.Fall Detection-principles and Methods//29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society.Lyon,France,2007:1663-1666.
[5]MUBASHIR M,SHAO L,SEED L.A survey on fall detection:Principles and approaches[J].Neurocomputing,2013,100:144-152.
[6]RATHI N,KAKANI M,EL-SHARKAWY M,et al.Wearablelow power pre-fall detection system with IoT and bluetooth capabilities[C]//IEEE National Aerospace and Electronics Conference (NAECON).Dayton,OH,2017:241-244.
[7]HOSSAIN F,ALI M L,ISLAM M Z,et al.A direction-sensitive fall detection system using single 3D accelerometer and learning classifier[C]//International Conference on Medical Engineering,Health Informatics and Technology (MediTec).Dhaka,2016:1-6.
[8]SCHWICKERT L,BECKER C,LINDEMANN U,et al.Fall detection with body-worn sensors?:a systematic review[J].Zeitschrift Für Gerontologie Und Geriatrie,2013,46(8):706-719.
[9]YAZAR A,ÇETIN A E.Ambient assisted smart home designusing vibration and PIR sensors[C]//21st Signal Processing and Communications Applications Conference (SIU).Haspolat,2013:1-4.
[10]ARSHAD A,KHAN S,ALAM A H M Z,et al.A capacitive proximity sensing scheme for human motion detection[C]//IEEE International Instrumentation and Measurement Techno-logy Conference(I2MTC).Turin,2017:1-5.
[11]REN S,GIRSHICK R,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,39(6):1137-1149.
[12]REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection[C]//IEEE Confer-ence on Computer Vision and Pattern Recognition (CVPR).Las Vegas,NV,2016:779-788.
[13]LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single shotmultibox detector[J].arXiv:1512.02325v2,2015.
[14]ZHOU X Y,WANG K,LI L Y.Review of object detection based on deep learning[J].Electronic Measurement Technology,2017(11):89-93.(in Chinese)周晓彦,王珂,李凌燕.基于深度学习的目标检测算法综述[J].电子测量技术,2017(11):89-93.
[15]FU C,LIU W,RANGA A,et al.DSSD:Deconvolutional single shot detector[J].arXiv:1701.06659,2016.
[16]JEONG J,PARK H,KWAK N.Enhancement of ssd by concatenating feature maps for object detection[J].arXiv:1705.09587,2017.
[17]LI Z,ZHOU F.FSSD:Feature fusion single shot multibox detector[J].arXiv preprint arXiv:1712.00960,2017.
[18]SHEN Z,LIU Z,LI J,et al.DSOD:Learning Deeply Supervised Object Detectors from Scratch//Proceedings of the IEEE International Conference on Computer Vision.2017:1937-1945.
[19]ZHANG Z,CONLY C,ATHITSOS V.A survey on vision-based fall detection[C]//8th ACM International Conference on PErvasive Technologies Related to Assistive Environments.2015:1-7.
[20]MITERAN J,DUBOIS J,ATRI M.Optimized spatio-temporal descriptors for real-time fall detection:comparison of support vector machine and Adaboost-based classification[J].Journal of Electronic Imaging,2013,22(4):41106.
[21]YUN Y,INNOCENTI C,NERO G,et al.Fall detection in RGB-D videos for elderly care[C]//17th International Conference on E-health Networking.Boston,MA,2015:422-427.
[22]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436.
[23]ADHIKARI K,BOUCHACHIA H,NAIT-CHARIF H.Activity recognition for indoor fall detection using convolutional neural network[C]//Fifteenth IAPR International Conference on Machine Vision Applications.Nagoya,2017:81-84.
[24]HUANG C D,WANG C Y,WANG J.Human action recognition system for elderly and children care using three stream ConvNet[C]//International Conference on Orange Technologies.Hong Kong,2016:5-9.
[25]SIMONYAN K,ZISSERMAN A.Two-stream convolutionalnetworks for action recognition in videos[J].Advances in Neural Information Processing Systems,2014,1(4):568-576.
[26]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,PP(99):1.
[27]HE K,SUN J.Convolutional neural networks at constrainedtime cost[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2015:5353-5360.
[28]ROUGIER C,MEUNIER J,ST-ARNAUD A,et al.Robust Video Surveillance for Fall Detection Based on Human Shape Deformation.IEEE Transactions on Circuits and Systems for Video Technology,2011,21(5):611-622.
[29]MIRMAHBOUB B,SAMAVI S,KARIMI N,et al.Automatic monocular system for human fall detection based on variations in silhouette area[J].IEEE Transactions on Biomedical Engineering,2013,60(2):427-436.
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