Abstract
Recent research in computer vision is increasingly focusing on develo** 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Panchal, P., Prajapati, G., Patel, S., Shah, H., Nasriwala, J.: A review on object detection and tracking methods. Int. J. Res. Emerging Sci. Technol. 2(1), 7–12 (2015)
Internet. https://en.wikipedia.org/wiki/GObject#Relation_to_GLib
Sundari, G., Bernatin, T., Somani, P.: H. 264 encoder using Gstreamer, pp. 1–4 (2015). https://doi.org/10.1109/ICCPCT.2015.7159511
Mittal, N., Vaidya, A., Kapoor, S.: Object detection and classification using Yolo. Int. J. Sci. Res. Eng. Trends 5, 562–565 (2019)
Popp, M.: Comprehensive support of the lifecycle of machine learning models in model management systems. MS thesis (2019)
Kreps, J., Narkhede, N., Rao, J.: Kafka: a distributed messaging system for log processing. In: Proceedings of 6th International Workshop on Networking Meets Databases (2011)
Capriolo, E., Wampler, D., Rutherglen, J.: https://books.google.com/?hl=en (2012)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation, OSDI 2004, pp. 137–149 (2004)
Zaharia, M., Das, T., Li, H., Shenker, S., Stoica, I.: Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters. In: 4th USENIX conference on Hot Topics in Cloud Computing (2012)
Internet. https://developpaper.com/optimization-and-comparison-of-reading-kafka-data-by-spark-streaming/
Shvachko, K., Hairong, K., Radia, S., Chansle, R.: The Hadoop distributed file system. In: 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), Incline Village, NV, USA (2010)
Internet. http://en.wikipedia.org/wiki/Create,_read,_update_and_delete (2010)
Venner, J.: Pro Hadoop, 1st edn. Apress, New York (2009)
Mann, K., Jones, M.T.: Distributed computing with Linux and Hadoop. http://www.ibm.com/developerworks/linux/library/l-hadoop/ (2010)
Pol, U.: Big data analysis: comparison of Hadoop MapReduce, pig and hive. Int. J. Innovative Res. Sci. Eng. Technol. 5, 9687–9693 (2016). https://doi.org/10.15680/IJIRSET.2015.0506026
Maposa, T., Sethi, M.: SQL-on-Hadoop: the most probable future in big data analytics (2018)
Cattell, R.: Scalable SQL and NoSQL data stores. SIGMOD Rec. 39(4), 12–27 (2011). https://doi.org/10.1145/1978915.1978919
Internet. https://www.nginx.com/learn/api-gateway/
Kumar, A., Singh, R.K.: Comparative analysis of AngularJS and ReactJS. Int. J. Latest Trends Eng. Technol. 7(4), 225–227 (2016)
Internet. https://2019-spring-web-dev.readthedocs.io/en/latest/final/taylor/index.html
Internet. https://oozie.apache.org/
Internet. https://oyermolenko.blog/2017/10/01/scheduling-jobs-in-hadoop-through-oozie/
Vijayalakshmi, N., Sivajothi, E., Vivekanandan, P.: efficiency and limitation of secure protocol in email services. Int. J. Eng. Sci. Res. Technol. 1, 539–544 (2012)
Banday, M.T.: Effectiveness and limitations of e-mail security protocols. Int. J. Distrib. Parallel Syst. 2(3) (2011)
Chhabra, G.S., Bajwa, D.S.: Review of e-mail system, security protocols and email forensics. Int. J. Comput. Sci. Commun. Netw. 5(3), 201–211 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Melenli, S., Topkaya, A. (2021). Real-Time Maintaining of Social Distance in Covid-19 Environment Using Image Processing and Big Data. In: Hemanth, J., Yigit, T., Patrut, B., Angelopoulou, A. (eds) Trends in Data Engineering Methods for Intelligent Systems. ICAIAME 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-79357-9_55
Download citation
DOI: https://doi.org/10.1007/978-3-030-79357-9_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79356-2
Online ISBN: 978-3-030-79357-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)