Object Detection for COVID Rules Response and Crowd Analysis

ABSTRACT

Understanding the hotspots attracting massive crowds is a huge necessity during this pandemic times. The knowledge of analyzing crowds will help to plan and avoid the spread of the virus to a large extent by identifying exact hotspots. Understanding where the crowds flock and whether they are following the guidelines or not will help in taking appropriate actions, allotting concerned personnel in advance, and closing of areas which are at higher risks can be advantageous. In order to realize the situation, real-time analysis of the pandemic rules like social distancing, wearing masks is necessary. This paper proposes the use of video surveillance and provides a combined application to check the factors necessary during crowd situations as per rules set by the Government. This work uses python as a coding language, and YOLOv4 algorithm along with various libraries like darknet to improve video and image analysis for the identification of exact requirements. This work also uses Cuda software and Cudnn library for the acceleration of processing. The paper proposes importantly, counting people passing through a particular area, detecting whether people are following social distancing, detecting if the participants are wearing a mask, and counting the number of vehicles passing through an area. The knowledge of analyzing crowds will help to plan and avoid the spread of the virus to a large extent by identifying exact hotspots. All the applications are connected to the graphical user interface (GUI) and depending on the input each application proposed considers different analysis. The types of input are image, video, image directory and live feed are considered to obtain better results. © 2021 IEEE.