Design Guidelines on Deep Learning-based Pedestrian Detection Methods for Supporting Autonomous Vehicles

Intelligent transportation systems (ITS) enable transportation participants to communicate with each other by sending and receiving messages, so that they can be aware of their surroundings and facilitate efficient transportation through better decision making. As an important part of ITS, autonomous vehicles can bring massive benefits by reducing traffic accidents. Correspondingly, much effort has been paid to the task of pedestrian detection, which is a fundamental task for supporting autonomous vehicles. With the progress of computational power in recent years, adopting deep learning based methods has become a trend for improving the performance of pedestrian detection. In this article, we present design guidelines on deep learning-based pedestrian detection methods for supporting autonomous vehicles. First, we will introduce classic backbone models and frameworks, and we will analyze the inherent attributes of pedestrian detection. Then, we will illustrate and analyze representative pedestrian detectors from occlusion handling, multi-scale feature extraction, multi-perspective data utilization, and hard negatives handling these four aspects. Last, we will discuss the developments and trends in this area, followed by some open challenges.

Automated summary

This article presents design guidelines on deep learning-based pedestrian detection methods to support autonomous vehicles. It covers classic backbone models and frameworks, inherent attributes of pedestrian detection, representative pedestrian detectors in aspects such as occlusion handling, multi-scale feature extraction, multi-perspective data utilization, and hard negative handling. The developments, trends and challenges in this area are also discussed.