Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques

• URL: http://arxiv.org/abs/2005.01385v4
• Date: Tue, 27 Apr 2021 05:08:05 GMT
• Title: Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
• Authors: Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal, Gaurav Rai
• Abstract summary: coronavirus disease 2019 (COVID-19) has brought global crisis with its deadly spread to more than 180 countries. Social distancing is the only feasible approach to fight against this pandemic. This article proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video.
• Score: 3.6016022712620095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rampant coronavirus disease 2019 (COVID-19) has brought global crisis with its deadly spread to more than 180 countries, and about 3,519,901 confirmed cases along with 247,630 deaths globally as on May 4, 2020. The absence of any active therapeutic agents and the lack of immunity against COVID-19 increases the vulnerability of the population. Since there are no vaccines available, social distancing is the only feasible approach to fight against this pandemic. Motivated by this notion, this article proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video. The proposed framework utilizes the YOLO v3 object detection model to segregate humans from the background and Deepsort approach to track the identified people with the help of bounding boxes and assigned IDs. The results of the YOLO v3 model are further compared with other popular state-of-the-art models, e.g. faster region-based CNN (convolution neural network) and single shot detector (SSD) in terms of mean average precision (mAP), frames per second (FPS) and loss values defined by object classification and localization. Later, the pairwise vectorized L2 norm is computed based on the three-dimensional feature space obtained by using the centroid coordinates and dimensions of the bounding box. The violation index term is proposed to quantize the non adoption of social distancing protocol. From the experimental analysis, it is observed that the YOLO v3 with Deepsort tracking scheme displayed best results with balanced mAP and FPS score to monitor the social distancing in real-time.

Related papers

• Wearing face mask detection using deep learning through COVID-19 pandemic [0.0]
  In this paper, we do an investigation on the capability of three state-of-the-art object detection neural networks on face mask detection for real-time applications. According to the performance of different models, the best model that can be suitable for use in real-world and mobile device applications was the YOLOv4-tiny model.
  arXiv  Detail & Related papers  (2023-04-28T19:39:32Z)
• Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System [69.40329819373954]
  The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. We propose a boundary guided semantic learning network (BSNet) in this paper.
  arXiv  Detail & Related papers  (2022-09-07T05:01:38Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks [48.7576911714538]
  Portugal was the country in the world with the largest incidence rate, with 14-days incidence rates per 100,000 inhabitants in excess of 1000. Despite its importance, accurate prediction of the geospatial evolution of COVID-19 remains a challenge.
  arXiv  Detail & Related papers  (2021-05-06T15:24:00Z)
• Delving into Localization Errors for Monocular 3D Object Detection [85.77319416168362]
  Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving. In this work, we quantify the impact introduced by each sub-task and find the localization error' is the vital factor in restricting monocular 3D detection.
  arXiv  Detail & Related papers  (2021-03-30T10:38:01Z)
• A Deep Learning Framework for COVID Outbreak Prediction [4.922572106422333]
  We propose a comparative analysis of deep learning models to forecast the COVID-19 outbreak. We propose a new Attention-based encoder-decoder model, named Attention-Long Short Term Memory (AttentionLSTM) The proposed model give superior forecasting accuracy compared to other existing methods.
  arXiv  Detail & Related papers  (2020-09-30T12:48:38Z)
• 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)
• A Vision-based Social Distancing and Critical Density Detection System for COVID-19 [0.0]
  Social distancing has been proven as an effective measure against the spread of the infectious COronaVIrus Disease 2019 (COVID-19). An active surveillance system capable of detecting distances between individuals and warning them can slow down the spread of the deadly disease. Here we propose an Artificial Intelligence (AI) based real-time social distancing detection and warning system considering four important ethical factors.
  arXiv  Detail & Related papers  (2020-07-07T15:55:50Z)
• A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
  We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system. We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
  arXiv  Detail & Related papers  (2020-05-15T17:17:45Z)

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.