Using whole and part-based HOG filters in succession to detect cars in aerial images

Vehicle detection in aerial images plays a key role in surveillance, transportation control and traffic monitoring. It forms an important aspect in the deployment of autonomous Unmanned Aerial System (UAS) in rescue and surveillance missions. In this paper, we propose a two-stage algorithm for efficient detection of cars in aerial images. We discuss how sophisticated detection technique may not give the best result when applied to large scale images with complicated backgrounds. We use a relaxed version of HOG (Histogram of Oriented Gradients) and SVM (Support Vector Machine) to extract hypothesis windows in the first stage. The second stage is based on discriminatively trained part-based models. We create a richer model to be used for detection from the hypothesis windows by detecting and locating parts in the root object. Using a two-stage detection procedure not only improves the accuracy of the overall detection but also helps us take complete advantage of the accuracy of sophisticated algorithms ruling out it's incompetence in real scenarios. We analyze the results obtained from Google Earth dataset and also the images taken from a camera mounted beneath a flying aircraft. With our approach we could achieve a recall rate of 90% with a precision of 94%.