Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment

Das, Sreetama; Nag, Anirban; Adhikary, Dhruba; Ram, Ramswaroop Jeevan; R, Aravind B; Ojha, Sujit Kumar; Hegde, Guruprasad M

doi:10.48550/arxiv.2104.10891

Cited by 2 publications

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“…There were numerous studies on control measures to monitor social distancing during the COVID-19 pandemic outbreak. Recent works proposed a methodology to estimate the distances of the pedestrian by using object detection along with projective geometry to calculate distances between humans in the bird's-eye perspective [5][6][7][8][9]. Therefore, it was possible to compute point correspondences between 2D and 3D worlds with this approach.…”

Section: Related Workmentioning

confidence: 99%

Hot Work Control Measures Using CNN-Based Object Detection and Projective Geometry for Industrial Surveillance Application

Choppradit,

Utintu,

Mahakijdechachai

et al. 2023

Advances in Transdisciplinary Engineering

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Industrial safety management has been a common challenge for many industries to implement since industrial hazards could cause fatal risks and unscheduled downtime. In this paper, we proposed an alternative approach for hot work control measures using CNN-based object detection and projective geometry, which could be integrated with the existing surveillance system. This method aims to monitor hot work activity and implement the risk assessment policy, which could control by the hazard area control. The dataset for our study consisted of 909 images of hot work activities captured by two closed-circuit television (CCTV) cameras. There are two steps to our methodology, which are the object detection stage and the bird’s-eye perspective transform stage. In the first stage, Workers, Welders, and Hot works are localized using an object detection algorithm, which is YOLOv5. To maximize the F1-score performance of object detection, we ran the experiments to train YOLOv5 with three levels of augmentations: low, medium, and high. For the second stage, four points are required in the method of transforming the object’s Cartesian coordinates into the new coordination in a bird’s-eye perspective. The radius distance threshold has to be manually calibrated for each specific camera point of view. If there is a worker that moves into the hot work radius, the violation alarm is triggered. The results show that medium augmentations produce the best results, with an overall mAP and F1-score of 0.77 and 0.74, respectively. In addition, the predefined distance threshold is also required and can vary in the different scenarios in the bird’s-eye perspective transform stage.

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Section: Related Workmentioning

confidence: 99%

Hot Work Control Measures Using CNN-Based Object Detection and Projective Geometry for Industrial Surveillance Application

Choppradit,

Utintu,

Mahakijdechachai

et al. 2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…They worked on the MOT16 dataset. Sreetama Das et al [15] proposed a computer visionbased solution to encourage abidance with social distancing norms. The method provides options to choose between tool-based mode and automated mode of the camera.…”

Section: Related Literaturementioning

confidence: 99%

SSDT: Distance Tracking Model Based on Deep Learning

Gupta

Gill

Gulia

2022

Int. j. electr. comput. eng. syst. (Online)

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Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and population vulnerability increased all over the world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% population has been fully vaccinated till now. Therefore, social distancing is only precautionary norm to avoid the spreading of this deadly virus. The risk of virus spread can be avoided by adhering to this norm. The main objective of this work is to provide a framework for tracking social distancing violations among people. This paper proposes a deep learning platform-based Smart Social Distancing Tracker (SSDT) model which is trained on MOT (Multiple Object Tracking) datasets. The proposed model is a hybrid approach that is a combination of YOLOv4 as object detection model merged with MF-SORT, Kalman Filter and brute force feature matching technique to distinguish people from background and provide a bounding box around these. Further, the results are also compared with another model, namely, Faster- RCNN in terms of FPS (frames per second), mAP(mean Average Precision) and training time over the dataset. The results show that the proposed model provides better and more balanced results. The experiment has been carried out in challenging conditions including, occlusion and under lighting variations with mAP of 97% and a real-time speed of 24 fps. The datasets provide numerous classes and from all the classes of objects, only people class has been used for identifying people in a closet. The ultimate goal of the model is to provide a tracking solution that will be helpful for different authorities to redesigning the layout of public places and reducing the risk. This model is also helpful in computing the distance between two people in an image and the results confirm that the proposed model successfully distinguishes between individuals who walk too close or breach the social distancing norms.

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Computer Vision-based Social Distancing Surveillance Solution with Optional Automated Camera Calibration for Large Scale Deployment

Cited by 2 publications

References 0 publications

Hot Work Control Measures Using CNN-Based Object Detection and Projective Geometry for Industrial Surveillance Application

Hot Work Control Measures Using CNN-Based Object Detection and Projective Geometry for Industrial Surveillance Application

SSDT: Distance Tracking Model Based on Deep Learning

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