标题:Hybrid Cascade Structure for License Plate Detection in Large Visual Surveillance Scenes
作者:Liu C.; Chang F.
作者机构:[Liu, C] School of Control Science and Engineering, Shandong University, Jinan 250061, China., China;[ Chang, F] School of Control Science and Enginee 更多
来源:IEEE Transactions on Intelligent Transportation Systems
出版年:2018
DOI:10.1109/TITS.2018.2859348
关键词:adaptive boosing (AdaBoost).; convolutional neural networks (CNNs); hybrid cascade; License plate detection (LPD)
摘要:Though license plate detection has been successfully applied in some commercial products, the detection of small and vague license plates in real applications is still an open problem. In this paper, we propose a novel hybrid cascade structure for fast detecting small and vague license plates in large and complex visual surveillance scenes. For rapid license plate candidate extraction, we propose two cascade detectors, including the Cascaded Color Space Transformation of Pixel detector and the Cascaded Contrast-Color Haar-like detector; these two cascade detectors can do coarse-to-fine detection in the front and in the middle of the hybrid cascade. In the end of the hybrid cascade, we propose a cascaded convolutional network structure (Cascaded ConvNet), including two detection-ConvNets and a calibration-ConvNet, which is designed to do fine detection. Through experiments with different evaluation data sets with many small and vague plates, we show that the proposed framework is able to rapidly detect license plates with different resolutions and different sizes in large and complex visual surveillance scenes. IEEE
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052662367&doi=10.1109%2fTITS.2018.2859348&partnerID=40&md5=11f97ebdbfb394500a2afc753ada828f
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