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

Abstract

ABSTRACT This paper analyzes the difference between the imaging mechan ism of the infrared images an d 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 a nd detect the object in the fo rward-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. Keywords: HOG, FLIR image, Linear SVM, Target recognition 1. INTRODUCTION In automatic target recognition system with forward-looking infrared (FLIR) images, the imaging mechanism is different from that of the visible light because the infrared images depend only on the temperature of the object itself and the radiation of heat. Owing to the solar radiation and the air temp erature as well as the constant heat exchange between the target and its surrounding environment, there are great differences between the grayscale characteristics of the same object at different moments. Moreover, the scale and angle deviation and a variety interference of noise in distanced FLIR images also complicate the target recognition process. So the template matching algorithm based on normalized cross-correlation is not good for automatic target recognition of FLIR images. Also the Hausdorff distance measures algorithm