Efficient Video-Based ALPR System Using YOLO and Visual Rhythm
This is an incremental improvement for video-based ALPR systems, potentially reducing processing requirements.
The paper tackled the problem of reducing computational cost in video-based Automatic License Plate Recognition (ALPR) by proposing a system that extracts only one frame per vehicle for license plate recognition, with early experiments indicating viability.
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.