Title :
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
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;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
DOI :
10.1109/SMC.2013.535
