Challenges of designing computer vision-based pedestrian detector for supporting autonomous driving
In recent years, aiming to improve deriving safety and supporting autonomous driving, pedestrian detection has attracted considerable attention from both industry and academic. Moreover, by taking advantage of the powerful computational capacity of GPU and high-level feature learning ability of the deep convolutional neural network, tremendous image/video-based pedestrian detection methods have been proposed. However, most of the existing approaches are designed relying on the computer vision-based target detection techniques. Accordingly, the evaluation criteria they consider in the design are often from the computer vision research field. Therefore, these existing methods tend to focus on the improvement of accuracy and ignore some of the special requirements that need to be considered in the field of autonomous driving. In this paper, we will analyze and summarize the features of the state-of-the-art pedestrian detection methods in detail. Then, by considering the practical application scenarios of autonomous driving techniques, we further discuss the open challenges of designing a practical pedestrian detection method for supporting autonomous deriving.
