Efficient Scale-Adaptive License Plate Detection System

Abstract

License plate detection is a common problem in traffic surveillance applications. Although some solutions have been proposed in the literature, their success is usually restricted to very specific scenarios, with their performance dropping in more demanding conditions. One of the main challenges to be addressed for this kind of systems is the varying scale of the license plates, which depends on the distance between the vehicles and the camera. Traditionally, systems have handled this issue by sequentially running single-scale detectors over a pyramid of images. This approach, although simplifies the training process, requires as many evaluations as considered scales, which leads to running times that grow linearly with the number of scales considered. In this paper, we propose a scale-adaptive deformable part-based model which, based on a well-known boosting algorithm, automatically models scale during the training phase by selecting the most prominent features at each scale and notably reduces the test detection time by avoiding the evaluation at different scales. In addition, our method incorporates an empirically constrained-deformation model that adapts to different levels of deformation shown by distinct local features within license plates. As shown in the experimental section, the proposed detector is robust and scale and perspective independent and can work in quite diverse scenarios. Experiments on two datasets show that the proposed method achieves a significantly better performance in comparison with other methods of the state of the art.