A human fall detection framework based on multi-camera fusion. Issue 6 (2nd November 2022)

• Record Type: Journal Article
• Authors: Ezatzadeh, Shabnam; Keyvanpour, Mohammad Reza; Shojaedini, Seyed Vahab
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 34:Issue 6(2022)
• Page Start: 905
• Page End: 924
• Publication Date: 2022-11-02
• Subjects: Visual surveillance -- fall detection -- elderly -- viewing direction -- occlusion -- fusion of multiple camera information; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2021.1938696
• Languages: English
• ISSNs: 0952-813X

Abstract

ABSTRACT: A sudden fall accident is the main concern for the elderly and disabled people. Automatic detection of the falls from video sequences is an assistive technology for surveillance systems. In this study, a three-stage framework was presented and implemented based on the combination of the data from multiple cameras to address the challenges of occlusion and visibility. In the first stage, the number of used cameras was specified. In the second stage, each camera was decided locally based on its data about the fall incident. In the third and final stage, the aggregation function was used to combine the single camera's decision considering the coverage rate coefficient of the used cameras. Experiments on the multiple-camera fall dataset demonstrated that our method is comparable to other state-of-the-art methods.