Fall incidents have been reported as the second most common cause of death, especially for elderly people. Human fall detection is necessary in smart home healthcare systems. Recently various fall detection approaches have been proposed., among which computer vision based approaches offer a promising and effective way. In this paper, we proposed a new framework for fall detection based on automatic feature learning methods. First, the extracted frames, including human from video sequences of different views, form the training set. Then, a PCANet model is trained by using all samples to predict the label of every frame. Because a fall behavior is contained in many continuous frames, the reliable fall detection should not only analyze one frame but also a video sequence. Based on the prediction result of the trained PCANet model for each frame, an action model is further obtained by SVM with the predicted labels of frames in video sequences. Experiments show that the proposed method achieved reliable results compared with other commonly used methods based on the multiple cameras fall dataset, and a better result is further achieved in our dataset which contains more training samples.
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Alwan M et al. (2006) A smart and passive floor-vibration based fall detector for elderly. in Information and Communication Technologies, 2006. ICTTA’06. 2nd. IEEE.
Auvinet E et al (2010) Multiple cameras fall dataset. DIRO-Université de Montréal, Tech. Rep, 1350.
Barnich O, Van Droogenbroeck M (2011) ViBe: A universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Machine Intell 35(8):1798–1828
Bourke AK, Lyons GM (2008) A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med Eng Phys 30(1):84–90
Cetin A (2013) Ambient assisted smart home design using vibration and PIR sensors. in Signal Processing and Communications Applications Conference (SIU), 2013 21st. IEEE
Chan T-H et al. (2014) PCANet: a simple deep learning baseline for image classification? arXiv preprint arXiv:1404.3606
Chen Y-T, Lin Y-C, Fang W-H (2010) A hybrid human fall detection scheme. in Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE
Chua J-L, Chang YC, Lim WK (2013) A simple vision-based fall detection technique for indoor video surveillance. Signal, Image and Video Processing: p. 1–11.
Hausdorff JM, Rios DA, Edelberg HK (2001) Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch Phys Med Rehabil 82(8):1050–1056
Hinton G, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
Kangas M et al (2008) Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28(2):285–291
Keskin F, Töreyin BU, Çetin AE (2013) Fall detection using single-tree complex wavelet transform. Pattern Recogn Lett 34(15):1945–1952
Mubashir M, Shao L, Seed L (2013) A survey on fall detection: principles and approaches. Neurocomputing 100:144–152
Nguyen T-T, Cho M-C, Lee T-S (2009) Automatic fall detection using wearable biomedical signal measurement terminal. in Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE. IEEE
Rougier C et al. (2007) Fall detection from human shape and motion history using video surveillance. in Advanced Information Networking and Applications Workshops, 2007, AINAW’07. 21st International Conference on. IEEE.
Rougier C et al (2011) Robust video surveillance for fall detection based on human shape deformation. C Syst Video Technology, IEEE Transactions on 21(5):611–622
Tao J et al. (2005) Fall incidents detection for intelligent video surveillance. in Information, Communications and Signal Processing, 2005 Fifth International Conference on. IEEE
Vishwakarma V, Mandal C, Sural S (2007) Automatic detection of human fall in video, in Pattern Recognition and Machine Intelligence. Springer. p. 616–623.
Wild D, Nayak U, Isaacs B (1981) How dangerous are falls in old people at home? Br Med J (Clin Res Ed) 282(6260):266
Williams A, Ganesan D, Hanson A (2007) Aging in place: fall detection and localization in a distributed smart camera network. in Proceedings of the 15th international conference on Multimedia. ACM
Yu X (2008) Approaches and principles of fall detection for elderly and patient. in e-health Networking, Applications and Services, 2008. HealthCom 2008. 10th International Conference on. IEEE
Zambanini S, Machajdik J, Kampel M (2010) Detecting falls at homes using a network of low-resolution cameras. in Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on. IEEE.
This work is supported by the National Natural Science Foundation of China (NSFC) Grants 61301241, 61401413, 61403353 and 61271405.
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Wang, S., Chen, L., Zhou, Z. et al. Human fall detection in surveillance video based on PCANet. Multimed Tools Appl 75, 11603–11613 (2016). https://doi.org/10.1007/s11042-015-2698-y
- Fall detection
- Behavior analysis
- Patient monitoring
- Visual surveillance