An Improved SSD Object Detection Algorithm For Safe Social Distancing and Face Mask Detection In Public Areas Through Intelligent Video Analytics

Recently, Coronavirus Disease (COVID-19) has spread rapidly across the world and thus social distancing and face mask has become one of the mandatory preventive measures. The Artificial Intelligence community has been focusing on developments for monitoring social distancing and identifying face mas...

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Bibliographic Details
Published in2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp. 1 - 7
Main Authors Anithadevi, N, Abinisha, J, Akalya, V, Haripriya, V
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.07.2021
Subjects
Artificial intelligence
COVID-19
Detectors
Human factors
improved object detection model
Lighting
MobileNetV2
Object detection
Single short detector (SSD)
Social Distancing
Social factors
Visual analytics
YoloV3
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Summary:Recently, Coronavirus Disease (COVID-19) has spread rapidly across the world and thus social distancing and face mask has become one of the mandatory preventive measures. The Artificial Intelligence community has been focusing on developments for monitoring social distancing and identifying face masks, which have become the hotspot and made the headlines. A powerful approach for fast and more accurate detection and monitoring would greatly enhance the sole purpose, save the lives of many and also alleviate the burden of doing it manually to some extent. You Only Look Once(Yolov3) and improved Single Shot multi-box Detector (SSD) algorithm has been deployed for an improved object detection model to lend a helping hand in the fight against this deadly disease. The proposed framework utilizes Single Shot multi-box Detector (SSD) with MobileNetV2, to enhance the feature extraction ability of the object detection model. After taking into account the significance of developing an accurate model and also the limitations of existing models, herein we have proposed a framework based on deep learning, which would automate and simplify the task of monitoring social distancing and face mask through intelligent video analytics.
DOI:10.1109/ICCCNT51525.2021.9579761