Infrared Pedestrian Detection Based on Attention Mechanism

Abstract

Pedestrian detection is one of the key technologies in computer vision, and plays an important role in surveillance and automatic driving. Compared with visible cameras, infrared cameras are more suitable for all-weather and all-day work. Recently, a number of methods have been proposed for infrared pedestrian detection, but cannot achieve a satisfactory performance in the case of small pedestrians. In this paper, we propose an improved RefineDet algorithm to solve the aforementioned problem. First, the aspect ratio in our method is modified to the range of an average person. Second, an attention mechanism is introduced to address the small spatial size of pedestrian. In addition, we develop a new dataset which includes small pedestrian for performance evaluation. Experiments demonstrated that our method can achieve a superior performance as compared to SSD and RefinDet methods.