NDLI: Support vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle

Content Provider	IET Digital Library
Author	Min, Weidong Yao, Leiyue Lin, Zhenrong Liu, Li
Abstract	Falls sustained by subjects can have severe consequences, especially for elderly persons living alone. A fall detection method for indoor environments based on the Kinect sensor and analysis of three-dimensional skeleton joints information is proposed. Compared with state-of-the-art methods, the authors’ method provides two major improvements. First, possible fall activity is quantified and represented by a one-dimensional float array with only 32 items, followed by fall recognition using a support vector machine (SVM). Unlike typical deep learning methods, the input parameters of their method are dramatically reduced. Hence, videos are trained and recognised by an SVM with a low time cost. Second, the torso angle is imported to detect the start key frame of a possible fall, which is much more efficient than using a sliding window. Their approach is evaluated on the telecommunication systems team (TST) fall detection dataset v2. The results show that their approach achieves an accuracy of 92.05%, better than other typical methods. According to the characters of machine learning, when more samples are imported, their method is expected to achieve a higher accuracy and stronger capability of fall-like discrimination. It can be used in real-time video surveillance because of its time efficiency and robustness.
Starting Page	1133
Ending Page	1140
Page Count	8
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5324
Journal	IET Computer Vision
Publisher Date	2018-07-12
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Deep Learning Method Elderly Persons Fall Detection Method Fall Recognition Fall-like Discrimination Geriatrics Human Skeleton Action Image Recognition Image Sonsor Indoor Environments Kinect Sensor Knowledge Engineering Technique Learning in AI Low Time Cost Machine Learning Object Detection One-dimensional Float Array Real-time Video Surveillance Sliding Window Start Key Frame Fast Detection Support Vector Machine Support Vector Machine Approach SVM Three-dimensional Skeleton Joint Information Analysis Torso Angle TST Fall Detection Dataset V2 Video Signal Processing Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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