Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision

Abstract

Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is following the rules and regulations that prevent further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of corona virus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area on which the person touches more than to some specific threshold. We further mark that area to some particular color, which will help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on the object detection algorithms (YOLO, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 87.7% accuracy on person detection. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 81.7% accuracy using the confusion matrix.

Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences

“…Deep learning has had a lot of success with human gait recognition in recent years. The convolutional neural network (CNN) is a type of deep learning model that is used for several processes, such as gait recognition [23], action recognition [24], medical imaging [25], and others [26,27]. A simple CNN model consists of a few important layers, such as convolutional, pooling, batch normalization, ReLu, GAP, fully connected, and classification layers [28,29].…”

Classification of human's activities from gesture recognition in live videos using deep learning

“…It is challenging to estimate the engagement level and decide what action a human will perform based on that engagement level. 3,4 Research is going on in computer vision and machine learning to embed this capability in machines; such systems will have plenty of applications such as E-learning systems, smart classrooms and interaction of humans and robots. 5 Before this era of technology, humans used to perform different tasks on their own.…”

“…In research, automatic engagement recognition (AER) is a term used to describe humans' engagement level, performing specific tasks. It is challenging to estimate the engagement level and decide what action a human will perform based on that engagement level 3,4 . Research is going on in computer vision and machine learning to embed this capability in machines; such systems will have plenty of applications such as E‐learning systems, smart classrooms and interaction of humans and robots 5 …”