Car detection from high-resolution aerial imagery using multiple features

Detecting cars in high-resolution aerial images has attracted particular attention in recent years. However, scene complexity, large illumination change and occlusions make the task very challenging. In this paper, we propose a robust and effective framework for car detection from high-resolution aerial imagery. More specifically, we first incorporate multiple diverse and complementary image descriptors, Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Opponent Histogram. Subsequently taking computational efficiency and runtime complexity into account, we adopt an interactive bootstrapping approach to collect hard negatives for training an intersection kernel support vector machine (IKSVM). After training, detection is performed by exhaustive search. Finally for post-processing, we employ a greedy procedure for eliminating repetitive detections via non-maximum suppression. Furthermore, contextual information is utilized to refine the detections. Experimental results on Vaihingen dataset have demonstrated that the proposed method can achieve state-of-the-art performance in various real scenes.