Practical object recognition in autonomous driving and beyond

Abstract

This paper is meant as an overview of the recent object recognition work done on Stanford's autonomous vehicle and the primary challenges along this particular path. The eventual goal is to provide practical object recognition systems that will enable new robotic applications such as autonomous taxis that recognize hailing pedestrians, personal robots that can learn about specific objects in your home, and automated farming equipment that is trained on-site to recognize the plants and materials that it must interact with. Recent work has made some progress towards object recognition that could fulfill these goals, but advances in model-free segmentation and tracking algorithms are required for applicability beyond scenarios like driving in which model-free segmentation is often available. Additionally, online learning may be required to make use of the large amounts of labeled data made available by tracking-based semi-supervised learning.