Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model
- URL: http://arxiv.org/abs/2501.01985v1
- Date: Mon, 30 Dec 2024 13:37:48 GMT
- Title: Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model
- Authors: Pinar Yozgatli, Yavuz Acar, Mehmet Tulumen, Selman Minga, Salih Selamet, Beytullah Nalbant, Mustafa Talha Toru, Berna Koca, Tevfik Keles, Mehmet Selcok,
- Abstract summary: This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators.
The model's performance, with an 85% precision and 82% recall in fall detection, underscores its potential for integration into existing elevator safety systems.
- Score: 0.0
- License:
- 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.
Related papers
- Continual Learning for Robust Gate Detection under Dynamic Lighting in Autonomous Drone Racing [27.18598697503772]
This study introduces a perception technique for detecting drone racing gates under illumination variations.
The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning.
arXiv Detail & Related papers (2024-05-02T07:21:12Z) - Attire-Based Anomaly Detection in Restricted Areas Using YOLOv8 for Enhanced CCTV Security [0.0]
This research introduces an innovative security enhancement approach, employing advanced image analysis and soft computing.
The focus is on an intelligent surveillance system that detects unauthorized individuals in restricted areas by analyzing attire.
arXiv Detail & Related papers (2024-03-31T11:09:19Z) - FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks.
We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness.
Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
arXiv Detail & Related papers (2024-03-26T08:51:23Z) - Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts,
Datasets and Metrics [77.34726150561087]
This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles.
Concepts and characteristics related to both sensors, as well as to their fusion, are presented.
We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
arXiv Detail & Related papers (2023-03-08T00:48:32Z) - Real-Time Damage Detection in Fiber Lifting Ropes Using Lightweight Convolutional Neural Networks [14.553374494874374]
Vision-based system for detecting damage in synthetic fiber rope images using lightweight convolutional neural networks.
Experts from Konecranes annotate the collected images in accordance with the rope's condition; normal or damaged.
Model outperforms other similar techniques with 96.5% accuracy, 94.8% precision, 98.3% recall, 96.5% F1-score, and 99.3% AUC.
arXiv Detail & Related papers (2023-02-23T11:44:43Z) - An Outlier Exposure Approach to Improve Visual Anomaly Detection
Performance for Mobile Robots [76.36017224414523]
We consider the problem of building visual anomaly detection systems for mobile robots.
Standard anomaly detection models are trained using large datasets composed only of non-anomalous data.
We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
arXiv Detail & Related papers (2022-09-20T15:18:13Z) - Vision Transformers and YoloV5 based Driver Drowsiness Detection
Framework [0.0]
This paper introduces a novel framework based on vision transformers and YoloV5 architectures for driver drowsiness recognition.
A custom YoloV5 pre-trained architecture is proposed for face extraction with the aim of extracting Region of Interest (ROI)
For the further evaluation, proposed framework is tested on a custom dataset of 39 participants in various light circumstances and achieved 95.5% accuracy.
arXiv Detail & Related papers (2022-09-03T11:37:41Z) - Adversarially-Aware Robust Object Detector [85.10894272034135]
We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images.
Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
arXiv Detail & Related papers (2022-07-13T13:59:59Z) - Multi Visual Modality Fall Detection Dataset [4.00152916049695]
Falls are one of the leading cause of injury-related deaths among the elderly worldwide.
Effective detection of falls can reduce the risk of complications and injuries.
Video cameras provide a passive alternative; however, regular RGB cameras are impacted by changing lighting conditions and privacy concerns.
arXiv Detail & Related papers (2022-06-25T21:54:26Z) - The State of Aerial Surveillance: A Survey [62.198765910573556]
This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective.
The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
arXiv Detail & Related papers (2022-01-09T20:13:27Z) - AutoOD: Automated Outlier Detection via Curiosity-guided Search and
Self-imitation Learning [72.99415402575886]
Outlier detection is an important data mining task with numerous practical applications.
We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model.
Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
arXiv Detail & Related papers (2020-06-19T18:57:51Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.