A novel real-time fall detection method based on head segmentation and convolutional neural network
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SPECIAL ISSUE PAPER
A novel real‑time fall detection method based on head segmentation and convolutional neural network Chenguang Yao1 · Jun Hu1 · Weidong Min1,2 · Zhifeng Deng3 · Song Zou3 · Weiqiong Min4 Received: 17 January 2020 / Accepted: 6 May 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract As the computer vision develops, real-time fall detection based on computer vision has become increasingly popular in recent years. In this paper, a novel real-time indoor fall detection method based on computer vision by using geometric features and convolutional neural network (CNN) is proposed. Gaussian mixture model (GMM) is applied to detect the human target and find out the minimum external elliptical contour. Differently from the traditional fall detection method based on geometric features, we consider the importance of the head in fall detection and propose to use two different ellipses to represent the head and the torso, respectively. Three features including the long and short axis ratio, the orientation angle and the vertical velocity are extracted from the two different ellipses in each frame, respectively, and fused into a motion feature based on time series. In addition, a shallow CNN is applied to find out the correlation between the two elliptic contour features for detecting indoor falls and distinguishing some similar activities. Our novel method can effectively distinguish some similar activities in real time, which cannot be distinguished by some traditional methods based on geometric features, and has a better detection rate. Keywords Computer vision · Fall detection · Head segmentation · Real-time image processing · Time series motion features
1 Introduction * Weidong Min [email protected] Chenguang Yao [email protected] Jun Hu [email protected] Zhifeng Deng [email protected] Song Zou [email protected] Weiqiong Min [email protected] 1
School of Software, Nanchang University, Nanchang 330047, China
2
Jiangxi Key Laboratory of Smart City, Nanchang 330047, China
3
School of Information Engineering, Nanchang University, Nanchang 330031, China
4
School of Tourism, Jiangxi Science and Technology Normal University, Nanchang 330038, China
Nowadays, with the aggravation of the aging problem in the world, caring for the health problems of the elderly has become a field of increasing importance, especially the health problems of the elderly living alone. Falling is the primary reason of death among seniors due to injuries, as increasing age, weakened muscle strength and the emergence of chronic diseases all increase the risk of falls [16]. When living alone seniors lose their ability to save themselves because of falls, they may miss the best opportunity for treatment, which can put their lives at risk. Therefore, a real-time intelligent surveillance system is demanded for the fall detection task in order to ensure the health of people, especially the seniors. These considerations have attracted many researchers to propose the intelligent surveill
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