Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Ayesha Siddiqua; Amena Saher; Sumera

doi:10.17485/IJST/v17i8.1458

Article

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i8.1458

Year: 2024, Volume: 17, Issue: 8, Pages: 741-750

Original Article

Vehicle Speed Detection using Haar Cascade Classifier and Correlation Tracking

Ayesha Siddiqua^1*, Amena Saher², Sumera³

¹M.S. Student, Department of Computer Science, Missouri University of Science and Technology, Rolla, 65409, MO, USA
²B.E. Student, Department of Computer Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, Telangana, India
³Assistant Professor, Department of Computer Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, Telangana, India

*Corresponding Author
Email: [email protected]

Received Date:14 June 2023, Accepted Date:27 November 2023, Published Date:20 February 2024

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

Abstract

Objectives: The aim of this study is to develop an efficient and cost-effective solution for predicting vehicle speeds using recorded video data. Methods: The proposed system employs a combination of image processing techniques and computer vision to calibrate cameras for traffic simulation, enabling the extraction of information on average vehicle speeds. It utilizes the Haar Cascade Classifier for object detection, followed by a correlation tracker for vehicle tracking. Speed estimation is achieved through the frame differencing method. The dataset comprises 90 minutes of recorded data from highway cameras, showcasing diverse traffic scenarios with various vehicle types (trucks, trailers, cars, buses, and bikes) at varying speeds. Predicted values are compared with ground truth data obtained from a GPS-equipped car, using Mean Absolute Error (MAE) as the evaluation metric. Findings: The algorithm's performance is evaluated, resulting in an average error rate of 1.72 km/h (2.07%). These findings are compared with state-of-the-art data. Novelty: This study introduces a novel system that combines the Haar Cascade Classifier, correlation tracker, and frame differencing method to track vehicle positions, incorporating bike detection into the analysis, and calculate their moving speeds. A relative analysis underscores the system's performance, emphasizing its effectiveness in real-world applications and demonstrating refinement in accuracy assessment.

Keywords: Image processing, Vehicle speed estimation, Haar Cascade Classifier, Correlation tracker, Error rate calculation, Computer vision

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Copyright

© 2024 Siddiqua et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)