Dataset for human fall recognition in an uncontrolled environment

Guerrero, José Camilo Eraso; España, Elena Muñoz; Muñoz-Añasco, Mariela; Lopera, Jesús Emilio Pinto

doi:10.1016/j.dib.2022.108610

Cited by 6 publications

(1 citation statement)

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“…An intuitive comparison between YOLOfall and other mainstream models can be seen in Figure 16, showing that YOLO-fall outperforms other models in terms of precision, recall, mAP, as well as performance related to computational parameters such as GFLOPs and Parameters. To further assess the model's generalization capability, we conducted validation using the CAUCAFall [32] dataset. The results are presented in Table 7.…”

Section: ) Comparison Experimentsmentioning

confidence: 99%

YOLO-Fall: A Novel Convolutional Neural Network Model for Fall Detection in Open Spaces

Zhao,

Song,

Gao

et al. 2024

IEEE Access

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Currently, incidents of personal accidents occur frequently in industrial fields, and falling is one of the most common safety hazards. This makes fall detection a research area of significant importance. Timely responses to fall events can significantly reduce the harm caused. However, most of the available fall detection models often suffer from issues such as insufficient detection accuracy or high parameter and computational requirements, making them challenging to deploy on local devices. In response, this paper introduces an enhanced convolutional neural network model, YOLOv7-fall, aimed at promptly detecting fall incidents. Firstly, the paper proposes a novel attention module, SDI, based on the Coordinate Attention and Shuffle Attention. This module enhances the feature extraction capabilities for detecting targets. Secondly, the inclusion of GSConv and VoV-GSCSP modules in the model's head section is in order to reduce model parameters and computational complexity, making it more suitable for deployment. Thirdly, by replacing the conventional 3x3 convolution in the final ELAN(Efficient Layer Aggregation Networks) module of the Backbone with the DBB (Diverse Branch Block), the model captures features from different layers or types in the image, increasing the network's diversity. Experimental results demonstrate that YOLO-fall improves mAP by 2.7% compared to YOLOv7-tiny while reducing model parameters by 3.5% and computational requirements by 5.4%. In comparison to existing detection algorithms under similar conditions, YOLO-fall achieves more precise and lightweight capabilities.

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Section: ) Comparison Experimentsmentioning

confidence: 99%