Open AccessSupport vector machine approach to fall recognition based on simplified expression of human skeleton action and fast detection of start key frame using torso angle
Author(s) -
Min Weidong,
Yao Leiyue,
Lin Zhenrong,
Liu Li
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5324
Subject(s) - torso , support vector machine , computer science , artificial intelligence , robustness (evolution) , sliding window protocol , frame (networking) , computer vision , pattern recognition (psychology) , key (lock) , machine learning , window (computing) , computer security , telecommunications , medicine , anatomy , biochemistry , chemistry , gene , operating system
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.