Image processing based human detection and social distancing measurements with monitoring via fine tuned deep learning and computer vision

Abstract

The COVID-19 pandemic has significantly affected day to day lifestyle all over the planet by disequilibrating social order. It moreover added anxiety about the capability of the world’s democracies to cope with the vital and crucial emergencies. We should urgently restore a necessary methodology to fathom the emergency and rigorously depict a course forward. The Division of Public Health authorities have recommended everyone to uphold social distancing with a view to diminishing the number of physical encounters. To keep a record of social distancing from an overhead standpoint, we established a computer vision deep learning framework. Our schemed system utilized the object recognition paradigm to spot and identify people in video sequences or frames. In our research, we assess the classification performance of two distinct multilayer neural network models named YOLO using OpenCV and TensorFlow which are used in the implementation process of an automatic recognition system. Amongst these using SSD, CUDA, and CUDNN we achieved a success rate in the classification. Neural networks were trained on a dataset where we used COCO dataset methods. At a time when neural networks are increasingly being utilized for a spectrum of uses, it is essential to select the proper model for the classification process that can attain the ultimate accuracy with the least amount of training duration. The demonstration created by us allows the insertion of images and the creation of their datasets, this allows the user to train a model using their chosen parameters. The models can then be saved and used in other systems. Moreover, to prevent future crucial situations and by keeping in the head about COVID affected situations on various global aspects this work will become an integral part of contributing to the term “Social Distancing” by implementing this sustainably and with one of the best results outcomes in our proposed image processing based human detection and social distancing measurements with monitoring via fine tuned deep learning and computer vision. Because coronavirus sickness has had such a negative influence on the world economy, this research tries to reduce the further impacts while minimizing resource loss. Also, create a very accurate detection mechanism to aid in the tracking of social distancing. In these types of serious situations, adequate actions must be taken and help to assist further research and work as an example for future works on this segment.