Real-Time Maintaining of Social Distance in Covid-19 Environment using Image Processing and Big Data

Abstract

Recent research in computer vision is increasingly focusing on developing systems to understand people's appearance, movements and activities, provide advanced interfaces for interacting with people, create human models. For any of these systems to work, they need methods to identify people from a particular input image or video. Today, real-time object detection and sizing of objects is an important issue in many areas of the industry. This is a vital issue of computer vision problems. With Covid-19's healing process, it will be very important to maintain social distance. In this research and development, it is aimed to maintain social distance with proposed big data architecture. This article provides an advanced technique to detect objects in video streams in real time and calculate their distance. The system composed research and developments to perform a stream from the camera, such as video stream, distance and object detection model, incoming data stream, data stream collection and report generation. The video stream from the camera is processed with GStream. The frames from the video stream are taken by OpenCV, YOLOV3 is trained by distance and object detection model and developed by Python. Video streaming data trained with Kubeflow is published with Apache Kafka and Apache Spark. It uses HDFS used to store published data. It is used to query and analyze data in Hive, Impala, Hbase HDFS. After that Analytical reports are created. E-mail notifications can be created according to the data in the database using by Apache Oozie. Through the proposed real time big data architecture, people can be safe in closed areas.