Manuscript Number : SHISRRJ247262

Object Detection and Tracking Using Python

Authors(2) :-N Bhavana, Kondapetta Sakeena

Efficient traffic management in urban areas relies on accurate and timely information about vehicular movement. This paper presents an innovative application of the YOLO (YOU ONLY LOOK ONCE) object detection algorithm, in conjunction with the OpenCV computer vision library, for real-time vehicle detection and its integration into traffic management systems. The YOLO algorithm’s unique architecture allows for simultaneous object detection and localization ina single pass, making it well-suited for real-time applications. Leveraging YOLO’s capabilities, this research focuses on detecting vehicles within the live video feeds from surveillance cameras strategically placed across road networks. Through the integration of yolo and OpenCV, this research showcases the potential for advanced vehicle detection techniques to significantly improve traffic management strategies. The resulting system contributes to more efficient traffic flow, enhanced safety measures, and a data driven approach to urban mobility planning.

Authors and Affiliations

N Bhavana
Assistant Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India
Kondapetta Sakeena
Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India

Object detection, Object tracking, computer vision, Deep learning, Convolutional Neural Networks (CNN), image processing, OpenCV (Open-source computer vision library), TensorFlow.

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Publication Details

Published in : Volume 7 | Issue 2 | March-April 2024
Date of Publication : 2024-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 357-363
Manuscript Number : SHISRRJ247262
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

N Bhavana, Kondapetta Sakeena, "Object Detection and Tracking Using Python", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 7, Issue 2, pp.357-363, March-April.2024
URL : https://shisrrj.com/SHISRRJ247262

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