Combining shape and texture features for infrared pedestrian detection

This paper presents a robust pedestrian detection algorithm that works on infrared imageries. Our algorithm is applicable to images captured from surveillance infrastructure as well as moving platforms. Firstly, we introduce a local binary pattern (LBP) texture feature for infrared pedestrian representation. Secondly, motivated by the recent success of multiple cues pedestrian detection in visual imagery, we combine both shape and binary pattern texture features for effective infrared pedestrian description, providing a level of robustness to variations in pedestrian shape and appearance in infrared images. Finally, a support vector machine (SVM) classifier is utilized to classify sub-windows into pedestrians or background. Experimental results demonstrate the robustness and effectiveness of our method.