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Fall Alert: A Novel Approach to Detect Fall Using Base as a YOLO Object Detection

  • Kiran P. Kamble
  • Swapnil S. SontakkeEmail author
  • Himanshu Donadkar
  • Ritik Poshattiwar
  • Abhinavram Ananth
Conference paper
  • 62 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

体育赛事投注记录in this work, a novel approach to detect irrecoverable fall of a person especially during night by leveraging convolutional neural network (cnn)-based object recognition and image processing techniques are proposed. yolo (you only look once) object detection model is trained on the coco dataset to detect the desired object from input frames extracted from a video stream and is tested whether it has succumbed to an irrecoverable fall or not for fall over multiple frames. the paper begins by introducing the core idea behind proposed approach and its importance followed by a review of previously done work in a nutshell. then, the proposed approach which gave appreciable results is presented. accuracy of the proposed approach is found to be 93.16%. the work also shows an experimental ratio of the height of camera to the distance of the person from the camera. it is found to be 5:6 for its successful fall detection that gives a competitive performance as compared to other state-of-the-art approaches.

Keywords

Fall detection YOLO COCO Convolutional neural network (CNN) Image processing 

Notes

Acknowledgements

体育赛事投注记录we would like to acknowledge cse department of walchand college of engineering, sangli for supporting us for successful execution of this work.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Kiran P. Kamble
    • 1
  • Swapnil S. Sontakke
    • 1
    Email author
  • Himanshu Donadkar
    • 1
  • Ritik Poshattiwar
    • 1
  • Abhinavram Ananth
    • 1
  1. 1.Department of Computer Science and EngineeringWalchand College of EngineeringSangliIndia

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