Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model

TL;DR

This paper presents a deep learning approach using the YoloV8 Nano model to detect falls in passenger elevators, aiming to improve safety through real-time surveillance despite environmental challenges.

Contribution

It introduces a specialized application of YoloV8 Nano for fall detection in elevators, trained on a large diverse dataset to enhance accuracy in a challenging environment.

Findings

85% precision in fall detection

82% recall rate achieved

Effective for real-time safety monitoring

Abstract

Computer vision technology, which involves analyzing images and videos captured by cameras through deep learning algorithms, has significantly advanced the field of human fall detection. This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators, a context that presents unique challenges due to the enclosed environment and varying lighting conditions. By training the model on a robust dataset comprising over 10,000 images across diverse elevator types, we aim to enhance the detection precision and recall rates. The model's performance, with an 85% precision and 82% recall in fall detection, underscores its potential for integration into existing elevator safety systems to enable rapid intervention.