SSDT: Distance Tracking Model Based on Deep Learning
نویسندگان
چکیده
Coronavirus disease (COVID-19) is an infectious caused by the SARS-CoV-2 virus and population vulnerability increased all over world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% has been fully vaccinated till now. Therefore, social distancing precautionary norm avoid spreading this deadly virus. The risk spread can be avoided adhering norm. main objective work provide a framework for tracking violations among people. This paper proposes deep learning platform-based Smart Social Distancing Tracker (SSDT) model which trained on MOT (Multiple Object Tracking) datasets. proposed hybrid approach that combination YOLOv4 as object detection merged with MF-SORT, Kalman Filter brute force feature matching technique distinguish people from background bounding box around these. Further, results also compared another model, namely, Faster- RCNN terms FPS (frames per second), mAP(mean Average Precision) training time dataset. show provides better more balanced results. experiment carried out challenging conditions including, occlusion under lighting variations mAP 97% real-time speed 24 fps. datasets numerous classes objects, class used identifying closet. ultimate goal solution will helpful different authorities redesigning layout public places reducing risk. computing distance between two image confirm successfully distinguishes individuals who walk too close or breach norms.
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ژورنال
عنوان ژورنال: International journal of electrical and computer engineering systems
سال: 2022
ISSN: ['1847-6996', '1847-7003']
DOI: https://doi.org/10.32985/ijeces.13.5.2