Vehicle Detection from UAVs by Using SIFT with Implicit Shape Model

Author

Xiyan Chen ; Qinggang Meng

Author_Institution

Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK

fYear

2013

fDate

13-16 Oct. 2013

Firstpage

3139

Lastpage

3144

Abstract

In recent years, unmanned aerial vehicles (UAVs) have gained a great importance in both military and civilian applications. In this paper, we proposed a vehicle detection method from UAVs which integrated of Scalar Invariant Feature Transform (SIFT) and Implicit Shape Model (ISM). Firstly, a set of key points was detected in the testing image by using SIFT. Secondly, feature descriptors around the key points were generated by using the ISM. Support Vector Machines (SVMs) were applied during the key points selection. The experiment used a video shoot by a UAV in a highway and the results showed the performance and the effectiveness of the method.

Keywords

autonomous aerial vehicles; feature extraction; object detection; support vector machines; ISM; SIFT; SVM; UAV; feature descriptor; implicit shape model; scalar invariant feature transform; support vector machine; unmanned aerial vehicle; vehicle detection; Accuracy; Feature extraction; Support vector machines; Testing; Training; Vehicle detection; Vehicles; Implicit Shape Model (ISM); Scale Invariant Feature Transform (SIFT); Unmanned Aerial Vehicle (UAV); Vehicle detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on

Conference_Location

Manchester

Type

conf

DOI

10.1109/SMC.2013.535

Filename

6722288