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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44080

Title: Symmetrical SURF and its Applications to Vehicle Detection and Vehicle Make and Model Recognition
Authors: Jun-Wei Hsieh
Li-Chih Chen
Duan-Yu Chen
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Symmetrical Speeded-Up Robust Features (SURF)
vehicle make and model recognition (MMR)
Date: 2014-02
Issue Date: 2017-11-14T07:47:37Z
Publisher: IEEE Trans. on Intelligent Transportation System
Abstract: Abstract:Speeded-Up Robust Features (SURF) is a robust and useful feature detector for various vision-based applications but it is unable to detect symmetrical objects. This paper proposes a new symmetrical SURF descriptor to enrich the power of SURF to detect all possible symmetrical matching pairs through a mirroring transformation. A vehicle make and model recognition (MMR) application is then adopted to prove the practicability and feasibility of the method. To detect vehicles from the road, the proposed symmetrical descriptor is first applied to determine the region of interest of each vehicle from the road without using any motion features. This scheme provides two advantages: there is no need for background subtraction and it is extremely efficient for real-time applications. Two MMR challenges, namely multiplicity and ambiguity problems, are then addressed. The multiplicity problem stems from one vehicle model often having different model shapes on the road. The ambiguity problem results from vehicles from different companies often sharing similar shapes. To address these two problems, a grid division scheme is proposed to separate a vehicle into several grids; different weak classifiers that are trained on these grids are then integrated to build a strong ensemble classifier. The histogram of gradient and SURF descriptors are adopted to train the weak classifiers through a support vector machine learning algorithm. Because of the rich representation power of the grid-based method and the high accuracy of vehicle detection, the ensemble classifier can accurately recognize each vehicle.
Relation: 15(1), pp.6-20
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44080
Appears in Collections:[資訊工程學系] 期刊論文

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