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Fall detection approach based on combined displacement of spatial features for intelligent indoor surveillance

  • 1193: Intelligent Processing of Multimedia Signals
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Abstract

Automatic human fall detection plays a significant role in monitoring senior citizens. Detecting fall events in an intelligent indoor condition can be used as a medium to reduce the consequences of older people being alone. In recent researches, the vision-based approach provides encouraging and practical results for fall detection. This article proposes a fall detection approach, which analyses the fall movement based on a combination of multiple benchmark spatial features. Further, it classifies them into abrupt fall or high impact fall, normal fall, and activities of daily living resembling a falling posture. The steps required to build the model are divided into four main phases: frame extraction, moving object detection, fall event analysis, and keyframe-based decision-making. The extraction of frames depends on a pre-defined interval between frames to reduce the time complexity. The Gaussian mixture model based background subtraction algorithm is used for moving object detection and foreground segmentation. Fall event analysis is carried out evaluating the combined displacement of the spatial features of the foreground. Finally, the keyframes representing different fall events are classified using fuzzy logic. The model delivers a sensitivity of 96.66% and specificity of 95% in detecting fall and fall-like activities, respectively. The proposed system can differentiate between a normal fall and an abrupt fall with a recognition rate of 90% and 96.66%. The system provides an automatic notification based on the type of fall event for appropriate care.

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Acknowledgements

This research has been carried out in MMWT lab of NIT Agartala, India sponsored by TEQIP—III vide order no F.NITA.2 (265-Estt)/2019/TEQIP -III/ Research Grant/9469—71 dated 17.12.2019.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT Agartala, Agartala, India

    Anurag De & Ashim Saha

  2. Department of Computer Science and Engineering, VNIT Nagpur, Nagpur, India

    Praveen Kumar

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  1. Anurag De
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  2. Ashim Saha
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Correspondence to Anurag De.

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De, A., Saha, A. & Kumar, P. Fall detection approach based on combined displacement of spatial features for intelligent indoor surveillance. Multimed Tools Appl 81, 5113–5136 (2022). https://doi.org/10.1007/s11042-021-11646-w

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  • DOI: https://doi.org/10.1007/s11042-021-11646-w

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