Paper Info
Title
Car Plate Detection Using Cascaded Tree-Style Learner Based on Hybrid Object Features
Abstract
Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.
Year
DOI
Venue
2006
10.1109/AVSS.2006.30
AVSS
Keywords
Field
DocType
car plate detection,automatic license plate recognition,false dismissal,simple statistical feature,hybrid object features,cascaded tree-style learner,false alarm,cascaded tree-style detector design,harr-like feature,high detection ratio,statistical feature,enhanced cascaded tree style,lighting,pattern recognition,interference,computer vision,detectors,robustness,image recognition
Object detection,Computer vision,3D single-object recognition,False alarm,Object-class detection,Pattern recognition,Computer science,Cascading classifiers,Robustness (computer science),Interference (wave propagation),Artificial intelligence,Detector
Conference
ISBN
Citations 
PageRank 
0-7695-2688-8
3
0.43
References 
Authors
7
6
Name
Order
Citations
PageRank
Qiang Wu130440.42
Huaifeng Zhang224018.84
Wenjing Jia332545.08
Xiangjian He4932132.03
Jie Yang51392157.55
Tom Hintz615430.39