Car Detection in High-Resolution Urban Scenes Using Multiple Image Descriptors

Robust and efficient detection of cars in urban scenes has many useful applications. This paper introduces a framework for car detection from high-resolution satellite images, wherein a novel extended image descriptor is used to depict the geometric, spectral and colour distribution properties of cars. The proposed framework is based on a sliding-window detection approach and it begins with a pre-prepossessing stage, which discards detection windows that are very unlikely to contain cars, e.g., plain areas and vegetation, followed by the computation of a concatenated feature vector of Histogram of Oriented Gradients, Fourier and truncated Pyramid Colour Self-Similarity image descriptors that is then fed to a pre-trained linear Support Vector Machine classifier to discriminate between the feature and non-feature subspaces. For post-processing, a non-maximum suppression technique is used to eliminate multiple detections. The performance of the proposed framework has been assessed on the Vaihingen dataset and results show that it exceeds the performance of the current state-of-the-art car detection algorithms.