Authors:
Ali Raza
1
;
2
;
Muhammad Yousaf
1
;
2
;
Sergio Velastin
3
;
4
and
Serestina Viriri
5
Affiliations:
1
Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan
;
2
Swarm Robotics Lab, National Centre of Robotics and Automation (NCRA), Pakistan
;
3
School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, U.K.
;
4
Department of Computer Engineering, University Carlos III, 28911 Leganés, Spain
;
5
School of Mathematics, Statistics & Computer Science University of KwaZulu-Natal, Durban, 4041, South Africa
Keyword(s):
Computer Vision, Fall Detection, Vision Transformers, Event Recognition.
Abstract:
Detecting human falls is an exciting topic that can be approached in a number of ways. In recent years, several approaches have been suggested. These methods aim at determining whether a person is walking normally, standing, or falling, among other activities. The detection of falls in the elderly population is essential for preventing major medical consequences and early intervention mitigates the effects of such accidents. However, the medical team must be very vigilant, monitoring people constantly, something that is time consuming, expensive, intrusive and not always accurate. In this paper, we propose an approach to automatically identify human fall activity using visual data to timely warn the appropriate caregivers and authorities. The proposed approach detects human falls using a vision transformer. A Multi-headed transformer encoder model learns typical human behaviour based on skeletonized human data. The proposed method has been evaluated on the UR-Fall and UP-Fall dataset
s, with an accuracy of 96.12%, 97.36% respectively using RP normalization and linear interpolation comparable to state-of-the-art methods.
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