Affiliations: [a] Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, India
| [b] Department of Information Technology, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, India
| [c] Vasile Goldis Western University of Arad, Arad, Romania
Correspondence:
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Corresponding author. Barnali Dey, Department of Information Technology, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar, Sikkim, Pin:737136, India. E-mail: barnali.mou.dey@gmail.com.
Abstract: Falls particularly among the older population has always been a matter of concern. With the steady rise of small families, the elderly is very often left alone at home. Dedicated nurses or caretakers are quite expensive. Thus, intelligent monitoring systems with automatic fall detection systems installed at home or nursing homes could be a game changer in such applications. In this paper, a simple yet effective fall detection system based on computer vision. Novelty of this paper is that it uses the Yolo v2 network on the depth videos for extracting the subject from cluttered background. The robust performance of the YOLOv2 network ensures accurate subject detection and removes the need for any complicated fall detection algorithm. Fall detection is carried out using subject’s height to width ratio and fall velocity. These parameters are simple and easy to calculate and yet provide effective results. The input data is captured using the Orbbec Astra 3D camera.
Keywords: Fall detection, depth image, convolutional neural network
