People detection and social distancing classification in smart cities for COVID-19 by using thermal images and deep learning algorithms

• URL: http://arxiv.org/abs/2209.04704v1
• Date: Sat, 10 Sep 2022 16:30:29 GMT
• Title: People detection and social distancing classification in smart cities for COVID-19 by using thermal images and deep learning algorithms
• Authors: Abdussalam Elhanashi, Sergio Saponara, Alessio Gagliardi
• Abstract summary: COVID-19 is a disease caused by severe respiratory syndrome coronavirus. It was identified in December 2019 in Wuhan, China. This research proposes an artificial intelligence system for social distancing classification of persons by using thermal images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: COVID-19 is a disease caused by severe respiratory syndrome coronavirus. It was identified in December 2019 in Wuhan, China. It has resulted in an ongoing pandemic that caused infected cases including some deaths. Coronavirus is primarily spread between people during close contact. Motivating to this notion, this research proposes an artificial intelligence system for social distancing classification of persons by using thermal images. By exploiting YOLOv2 (you look at once), a deep learning detection technique is developed for detecting and tracking people in indoor and outdoor scenarios. An algorithm is also implemented for measuring and classifying the distance between persons and automatically check if social distancing rules are respected or not. Hence, this work aims at minimizing the spread of the COVID-19 virus by evaluating if and how persons comply with social distancing rules. The proposed approach is applied to images acquired through thermal cameras, to establish a complete AI system for people tracking, social distancing classification, and body temperature monitoring. The training phase is done with two datasets captured from different thermal cameras. Ground Truth Labeler app is used for labeling the persons in the images. The achieved results show that the proposed method is suitable for the creation of a smart surveillance system in smart cities for people detection, social distancing classification, and body temperature analysis.

Related papers

• A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19 [64.88701052813462]
  Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets.
  arXiv  Detail & Related papers  (2022-01-13T03:28:20Z)
• Unsupervised deep learning techniques for powdery mildew recognition based on multispectral imaging [63.62764375279861]
  This paper presents a deep learning approach to automatically recognize powdery mildew on cucumber leaves. We focus on unsupervised deep learning techniques applied to multispectral imaging data. We propose the use of autoencoder architectures to investigate two strategies for disease detection.
  arXiv  Detail & Related papers  (2021-12-20T13:29:13Z)
• Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough [55.45063681652457]
  We propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification. We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection.
  arXiv  Detail & Related papers  (2021-07-12T08:07:56Z)
• Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm [78.88882860340797]
  COVID-19 virus has caused a global pandemic since March 2020. Maintaining a minimum of one meter distance from other people is strongly suggested to reduce the risk of infection. There is no suitable test benchmark for such algorithms.
  arXiv  Detail & Related papers  (2021-03-11T16:15:20Z)
• SD-Measure: A Social Distancing Detector [0.0]
  Social distancing has been adopted as a non-pharmaceutical prevention measure during the COVID-19 pandemic. This work proposes a novel framework named SD-Measure for detecting social distancing from video footages.
  arXiv  Detail & Related papers  (2020-11-04T15:47:14Z)
• Perceiving Humans: from Monocular 3D Localization to Social Distancing [93.03056743850141]
  We present a new cost-effective vision-based method that perceives humans' locations in 3D and their body orientation from a single image. We show that it is possible to rethink the concept of "social distancing" as a form of social interaction in contrast to a simple location-based rule.
  arXiv  Detail & Related papers  (2020-09-01T10:12:30Z)
• DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic [1.027974860479791]
  Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. We develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network model for automated people detection in the crowd using common CCTV cameras. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields.
  arXiv  Detail & Related papers  (2020-08-26T16:56:57Z)
• New Normal: Cooperative Paradigm for Covid-19 Timely Detection and Containment using Internet of Things and Deep Learning [12.618653234201089]
  The spread of the novel coronavirus (COVID-19) has caused trillions of dollars in damages to the governments and health authorities by affecting the global economies. This study introduces a connected smart paradigm that not only detects the possible spread of viruses but also helps to restart businesses/economies, and resume social life.
  arXiv  Detail & Related papers  (2020-08-15T14:33:53Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging [8.110295985047278]
  Home-based camera systems for fall detection often put people's privacy at risk. As fall occurs rarely, it is non-trivial to learn algorithms due to class imbalance. We formulate fall detection as an anomaly detection within an adversarial framework using thermal imaging.
  arXiv  Detail & Related papers  (2020-04-17T17:17:29Z)

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.