Deep Learning-Based Traffic Behavior Analysis under Multiple Camera Environment

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Published Oct 31, 2022
https://doi.org/10.47164/ijngc.v13i3.719
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Volume 13, Special Issue 3, October 2022

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Rakhi Joshi Bhardwaj
Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana, Working as Assistant Professor at Vishwakarma Institute of Technology,Pune
https://orcid.org/0000-0003-4113-2774
Dr.D.S. Rao
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation,Deemed to be University, Hyderabad, Telangana.
https://orcid.org/0000-0003-2257-4542

Abstract

In a video surveillance system, tracking multiple moving objects using a single camera feed is having numerous challenges. A multi-camera system increases the output image quality in both overlapping and non-overlapping environment. Traffic behavior analysis is an intensified demand in a recent topic of research. Due to increasing traffic in intercity roads, interstate, and national highways. Automated traffic visual surveillance applications with the multi-camera are a topic of research in computer vision. This paper, present a multi-camera system study for the overlapping area of the road for traffic analysis in three sections. The second section represents the thorough literature survey on the multi-camera system. Here, the third section is our proposed system using a dual-camera experimental setup with their coordination. A deep neural network is used in the experiments for traffic behavior analysis. The emphasis of this paper is on the physical arrangement of the multi-camera system, calibration, and advantages- disadvantages. On a conclusion note, future development and advancement in traffic analysis using a multi-camera system is discussed.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Bhardwaj, R. J., & Rao, D. (2022). Deep Learning-Based Traffic Behavior Analysis under Multiple Camera Environment. International Journal of Next-Generation Computing, 13(3). https://doi.org/10.47164/ijngc.v13i3.719
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