Robust vehicle detection in low-resolution aerial imagery

被引：5
|
作者：
Sahli, Samir [1 ]
Ouyang, Yueh [1 ]
Sheng, Yunlong [1 ]
Lavigne, Daniel A. [2 ]
机构：
[1] Univ Laval, Image Sci Grp, Ctr Opt Photon & Lasers, Quebec City, PQ G1K 7P4, Canada
[2] Def Res & Dev Canada, Quebec City, PQ G3J 1X5, Canada
来源：
AIRBORNE INTELLIGENCE, SURVEILLANCE, RECONNAISSANCE (ISR) SYSTEMS AND APPLICATIONS VII | 2010年 / 7668卷
基金：
加拿大自然科学与工程研究理事会;
关键词：
aerial imagery; vehicle detection; feature-based detection; scale-invariant feature transform; support vector machine; Affinity Propagation; SCALE;
D O I：
10.1117/12.850387
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
We propose a feature-based approach for vehicle detection in aerial imagery with 11.2 cm/pixel resolution. The approach is free of all constraints related to the vehicles appearance. The scale-invariant feature transform (SIFT) is used to extract keypoints in the image. The local structure in the neighbouring of the SIFT keypoints is described by 128 gradient orientation based features. A Support Vector Machine is used to create a model which is able to predict if the SIFT keypoints belong to or not to car structures in the image. The collection of SIFT keypoints with car label are clustered in the geometric space into subsets and each subset is associated to one car. This clustering is based on the Affinity Propagation algorithm modified to take into account specific spatial constraint related to geometry of cars at the given resolution.
引用
收藏
页数：8
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