Teichman, Alex (Stanford), Levinson, Jesse (Stanford University), Thrun, Sebastian (Stanford University)
Towards 3D Object Recognition Via Classification of Arbitrary Object Tracks
Scheduled for presentation during the Regular Sessions "Recognition II" (WeP204), Wednesday, May 11, 2011,
16:25−16:40, Room 3E
2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China
This information is tentative and subject to change. Compiled on December 8, 2019
Object recognition is a critical next step for autonomous robots, but a solution to the problem has remained elusive. Prior 3D-sensor-based work largely classifies individual point cloud segments or uses class-specific trackers. In this paper, we take the approach of classifying the tracks of all visible objects. Our new track classification method, based on a mathematically principled method of combining log odds estimators, is fast enough for real time use, is non-specific to object class, and performs well (98.5% accuracy) on the task of classifying correctly-tracked, well-segmented objects into car, pedestrian, bicyclist, and background classes.
We evaluate the classifier's performance using the Stanford Track Collection, a new dataset of about 1.3 million labeled point clouds in about 14,000 tracks recorded from an autonomous vehicle research platform. This dataset, which we make publicly available, contains tracks extracted from about one hour of 360-degree, 10Hz depth information recorded both while driving on busy campus streets and parked at busy intersections.