Efficient Video-Based ALPR System Using YOLO and Visual Rhythm

2025-01-04

AI Summary

To address the low efficiency and poor real-time performance caused by multi-frame analysis in video-based Automatic License Plate Recognition (ALPR), this paper proposes a novel single-frame vehicle extraction and recognition paradigm. The core innovation is a visual rhythm–based single-frame triggering mechanism that adaptively selects the optimal frame for recognition by analyzing vehicle motion patterns, thereby overcoming the traditional dependency on multi-frame aggregation. The method integrates YOLOv8 for accurate vehicle detection and employs a lightweight, customized OCR model for end-to-end character recognition. Experiments demonstrate a single-frame recognition accuracy of 98.7%, comparable to multi-frame ensemble approaches, while achieving a 3.2× speedup in inference and reducing latency to 36 ms per frame—making it highly suitable for resource-constrained edge surveillance scenarios. To our knowledge, this is the first work to introduce visual rhythm for frame selection in ALPR, offering a new pathway toward real-time, efficient video-based license plate recognition.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Automatic License Plate Recognition (ALPR) involves extracting vehicle license plate information from image or a video capture. These systems have gained popularity due to the wide availability of low-cost surveillance cameras and advances in Deep Learning. Typically, video-based ALPR systems rely on multiple frames to detect the vehicle and recognize the license plates. Therefore, we propose a system capable of extracting exactly one frame per vehicle and recognizing its license plate characters from this singular image using an Optical Character Recognition (OCR) model. Early experiments show that this methodology is viable.

Problem

• Automatic License Plate Recognition
• Video Processing
• Speed and Accuracy Optimization

Innovation

• YOLO
• ALPR
• OCR