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Elderly Patient Fall Detection Using Video Surveillance

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Computer Vision and Image Processing (CVIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1568))

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Abstract

The important goal of this work is to design a system for elderly people who are at higher risk of falling due to some illness. We aim to do this without burdening them with the different number of devices around them including wear-able devices. We aim to continuously monitor them in a closed environment for any kind of falls or injuries that might occur to them. This paper aims to achieve this with the help of only video surveillance [13, 16]. We also aim to propose a method where the model is very cost-friendly and easy to implement in any closed environment. Our model removes any use of wearable sensors and proposes an approach where we use inputs from only RGB camera-based sensors and then uses different computer vision approaches [2, 6] to decide if the person has fallen or not. We are using a two-stream network to attain more accuracy. Estimating the motion of a person on regular basis is the main step in the proposed method. There are two most popular publically available methods for motion detection namely Optical Flow (OF) [26] and Motion History Image (MHI) [17]. In the proposed method, we are using OF for the motion estimation. With the help of OF [26] and VGG16 [25] we have proposed a method for elderly patient monitoring system using live camera to detect a falling person. Despite not using multiple wearable and non-wearable sensors our proposed method outperforms the existing fall detection models. We have worked on the publically available UR-Fall Dataset [10].

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Authors and Affiliations

  1. Punjab Engineering College, Chandigarh, India

    Amartya Raghav & Sachin Chaudhary

Authors
  1. Amartya Raghav
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  2. Sachin Chaudhary
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Corresponding author

Correspondence to Amartya Raghav .

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Editors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  2. Indian Institute of Technology Ropar, Ropar, India

    Subrahmanyam Murala

  3. Jadavpur University, Kolkata, India

    Ananda Chowdhury

  4. Indian Institute of Technology Ropar, Ropar, India

    Abhinav Dhall

  5. Indian Institute of Technology Ropar, Ropar, India

    Puneet Goyal

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Raghav, A., Chaudhary, S. (2022). Elderly Patient Fall Detection Using Video Surveillance. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_39

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  • DOI: https://doi.org/10.1007/978-3-031-11349-9_39

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