Design of People Count System Based on Object Detection Algorithm

At present, the Covid-19 epidemic is still spreading globally. Although the domestic epidemic has been well controlled, the prevention and control of the epidemic must not be taken lightly. Being able to count the number of people in public places in real time has played a vital role in the prevention and control of the epidemic. Deep learning networks usually cannot be directly deployed on embedded devices with low computing power due to the huge amount of parameters of convolutional neural networks. This article is based on the YOLOv5 object detection algorithm and Jetson Nano embedded platform with TensorRT and C++ accelerating, it can realize the function of counting the number of people in the classroom, on the elevator entrance, and other scenes. © 2022 SPIE.

Computer Vision and Machine Learning-Based Techniques for Detecting the Safety Violations of COVID-19 Scenarios: A Review

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus2. COVID-19 has created the worldwide pandemic situation and it is causing a greater health crisis and deaths of the millions of humans all over the world. All the socio-economic activities are very much affected and there is a huge loss over the world in many aspects. If safety measures are not followed strictly in the public places, then there is a rapid spread of the disese at a very faster rate. Hence, this paper provides a thorough survey of the existing computer vision and machine learning-based technological solutions for controlling the spread of the disease. It also discusses some challenges and future perspectives in developing systems for monitoring the COVID-19 safety violations.

Object Detection for COVID Rules Response and Crowd Analysis

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.