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Forward-looking infrared target recognition based on histograms of oriented gradients

This paper analyzes the difference between the imaging mechanism of the infrared images and that of the visible light images, and find that it is important to extract the stable and reliable common feature for object recognition. Then we propose a target recognition algorithm based on histograms of oriented gradients (HOG) which evaluates normalized local histograms of image gradient orientations in a dense grid. Last we adopt linear SVM trained for a binary object/non-object classifier and detect the object in the forward-looking infrared (FLIR) images. The experiment results suggest that the proposed approach has high rates of detection. Furthermore, we study how to select positive and negative samples for a better performance.