Subanshika,
Shivam Patil,
Nikita Jain,
- Student, Department of Computer Engineering, Poornima College of Engineering, Rajasthan, India
- Student, Department of Computer Engineering, Poornima College of Engineering, Rajasthan, India
- Professor, Department of Computer Engineering, Poornima College of Engineering, Rajasthan, India
Abstract
It is difficult to watch traffic on crowded roads. Traffic monitoring procedures are time-consuming, expensive, labor-intensive, and require human operators. The limited accessibility hindered the storing and processing of large-scale video streams. Nonetheless, it is now possible to employe video feeds from traffic monitoring systems for number plate recognition, object tracking, traffic behavior analysis, and surveillance. Static image recognition and vehicle identification in a traffic surveillance system are very useful and easily adjustable to a range of tasks. The techniques for processing automated vehicle detection and recognition will be covered in this article. Cars’ notable discriminating qualities are discovered to be correlated with minimum bounding box environment- related data. Vehicle detection is a crucial task in traffic surveillance, which has several applications in transportation systems, public safety, and urban planning. In traffic surveillance film, machine learning approaches have demonstrated significant potential for effectively identifying moving cars. In this research, we offer a machine learning-based methodology for effective moving vehicle detection in traffic surveillance. Data collection, preprocessing, feature extraction, model selection, training, assessment, and deployment are all steps in the technique. We demonstrate the effectiveness of the proposed methodology by evaluating the performance of several machine learning models on a large traffic surveillance dataset.
Keywords: Traffic Detection, ML, Artificial intelligence, LGBP Histogram
[This article belongs to Trends in Machine design (tmd)]
Subanshika, Shivam Patil, Nikita Jain. Traffic Detection Algorithms Analysis using ML. Trends in Machine design. 2024; 11(02):1-8.
Subanshika, Shivam Patil, Nikita Jain. Traffic Detection Algorithms Analysis using ML. Trends in Machine design. 2024; 11(02):1-8. Available from: https://journals.stmjournals.com/tmd/article=2024/view=176423
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Trends in Machine design
| Volume | 11 |
| Issue | 02 |
| Received | 09/05/2024 |
| Accepted | 04/06/2024 |
| Published | 30/09/2024 |