Deakin University
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Hot-spot zone detection to tackle COVID19 spread by fusing the traditional machine learning and deep learning approaches of computer vision

journal contribution
posted on 2021-01-01, 00:00 authored by Muhammad Zeeshan Khan, Muhammad Usman Ghani Khan, Tanzila Saba, Imran RazzakImran Razzak, Amjad Rehman, Saeed Ali Bahaj
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.



IEEE Access




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Institute of Electrical and Electronics Engineers


Piscataway, N.J.





Publication classification

C1 Refereed article in a scholarly journal