High performance automatic number plate recognition in video streams

摘要

We present a range of image and video analysis techniques that we have developed in connection with license plate recognition. Our methods focus on two areas — efficient imago preprocessing to improve low-quality detection rate and combining the detection results from multiple frames to improve the accuracy of the recognized license plates. To evaluate our algorithms, we have implemented a complete ANPR system that detects and reads license plates. The system can process up to 110 frames per second on single CPU core and scales well to at least 4 cores. The recognition rate varies depending on the quality of video streams (amount of motion blur, resolution), but approaches 100% for clear, sharp license plate input data. The software is currently marketed commercially as CarID~1. Some of our methods are more general and may have applications outside of the ANPR domain.