Deep Learning Based Vehicle Tracking System Using License Plate Detection And Recognition

TL;DR

This paper presents a deep learning-based vehicle tracking system that detects and recognizes license plates efficiently, achieving near-human accuracy and real-time performance suitable for large-scale traffic management integration.

Contribution

It introduces a novel vehicle tracking approach combining scene text detection and OCR for license plate recognition, improving robustness and speed over previous heuristic methods.

Findings

Achieved 30 fps processing speed on highway videos.

Obtained recognition accuracy close to human performance.

Demonstrated system's scalability for large traffic management systems.

Abstract

Vehicle tracking is an integral part of intelligent traffic management systems. Previous implementations of vehicle tracking used Global Positioning System(GPS) based systems that gave location of the vehicle of an individual on their smartphones.The proposed system uses a novel approach to vehicle tracking using Vehicle License plate detection and recognition (VLPR) technique, which can be integrated on a large scale with traffic management systems. Initial methods of implementing VLPR used simple image processing techniques which were quite experimental and heuristic. With the onset of Deep learning and Computer Vision, one can create robust VLPR systems that can produce results close to human efficiency. Previous implementations, based on deep learning, made use of object detection and support vector machines for detection and a heuristic image processing based approach for recognition. The proposed system makes use of scene text detection model architecture for License plate detection and for recognition it uses the Optical character recognition engine (OCR) Tesseract. The proposed system obtained extraordinary results when it was tested on a highway video using NVIDIA Ge-force RTX 2080ti GPU, results were obtained at a speed of 30 frames per second with accuracy close to human.