CMU talk: A Hierarchical Image Analysis for Extracting Parking Lot Structure from Aerial Image.

A Hierarchical Image Analysis for Extracting Parking Lot Structure from Aerial Image.

Young-Woo Seo
Ph.D Student
Robotics Institute
Carnegie Mellon University

Thursday, December 4th

Abstract
The road network information simplify autonomous driving by providing strong priors on driving environments for planning and perception. It tells a robotic vehicle where it can drive and provides contextual cues that inform the driving behavior. For example, this information lets the robotic vehicle know information about upcoming intersections (e.g. that the intersection is a four-way stop and that the robot must conform to precedence rules) and other fixed rules of the road (e.g. speed limits). Currently the road network information about driving environments is manually generated using a combination of GPS survey and aerial imagery. These techniques for converting digital imagery into road network information are labor intensive, reducing the benefit provided by digital maps. To fully exploit the benefits of digital imagery, these processes should be automated. As a step toward this goal, we present a machine learning algorithm that extracts the structure of parking lot from a given aerial image. We approach this problem hierarchically from low-level image analysis through high-level structure inference. We test three different methods and their combinations. From the experimental results, our Markov Random Fields implementation outperforms other methods in terms of false negative and positive rates.