Vehicle detection in aerial imagery using eigen features

The invention of low cost optical sensors lead to a rapid exploitation of aerial images in numerous applications. Automatic vehicle detection in aerial images is remarkably employed in traffic safety, urban planning, military, parking lot management, aerial surveillance, catastrophe and disaster management. The huge amount of data collected in such tasks enforce to automate the detection process. The objective of this work is to detect small vehicles from aerial images in an unconstrained environment. The experiments are conducted on the VEDAI dataset. The architecture of the system includes two phases namely training and detection. The training phase includes cropping and extraction of the training samples, feature representation and classification. And the detection phase consists of extracting the regions of interest, feature extraction and classification. The vehicles occupy only less than 1 % of total pixels in the image. Hence to limit the search area, an edge detection is performed as a pre-processing step. The bounding boxes that are selected after applying size constraint are classified using support vector machine (SVM) classifier. Two feature extraction techniques are utilized in this work specifically singular value decomposition (SVD) and histogram of oriented gradients (HOG). From the experimental results analyzed based on overall classification accuracy, the proposed SVD features provide a comparable performance with existing HOG features.