Advanced Systems for Detecting and Recognizing Traffic Objects

Gaurav Singh; Prof. Sonam Singh

doi:10.32628/CSEIT24104111

Authors

Gaurav Singh Research Scholar, Department of Computer Science, SHEAT College of Engineering, Babatpur, Varanasi, India Author
Prof. Sonam Singh Assistant Professor, Department of Computer Science, SHEAT College of Engineering, Babatpur, Varanasi, India Author

DOI:

https://doi.org/10.32628/CSEIT24104111

Keywords:

Object Detection, Convolutional Neural Network, Deep Learning, Machine Learning, GSM, GPS, Google Maps, Tracking System

Abstract

Traffic object detection and recognition systems have become critical components in the advancement of intelligent transportation systems (ITS). These systems leverage various technologies such as computer vision, machine learning, and sensor fusion to accurately identify and classify objects on the road, including vehicles, pedestrians, traffic signs, and obstacles. The integration of these technologies enhances traffic management, improves road safety, and facilitates the development of autonomous vehicles. This paper provides an overview of the state-of-the-art methods and technologies used in traffic object detection and recognition. It also discusses the challenges faced in real-world implementations, such as varying weather conditions, lighting changes, and occlusions, and explores potential solutions and future research directions to address these issues.

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