Research Abstracts - 2007

Abstract

The MIT DARPA Grand Challenge team, based at CSAIL, is building a large perception codebase with a number of different algorithms related to finding road lane markings and obstacles from video, radar, and LIDAR sensor data. The team has also collected several logged data sets comprising video, radar, LIDAR and IMU data. Our project has two aims:

• To create an efficient tool to label the logged video data with "ground truth" locations of lanes, vehicles, hazards, drivable zones, and pedestrians on a per-frame basis; and
• To use the tool to create labels for a number of logged videos.

Specifically, the types of items that are labeled by the tool are: Lane Single, Lane Double, Lane Crosswalk, Lane Stopline, Lane Other, Vehicle Stopped, Vehicle Approaching, Vehicle Retreating, Vehicle Other, Hazard Up, Hazard Down, Hazard Other, Drivezone Smooth, Drivezone Rough, Drivezone Other, Artifact Saturation, Artifact Occlusion, Artifact Other, Pedestrian Stopped, Pedestrian Crossing.

The tool saves the labeled data to a text file in xml format according to a specified grammar. Saved data includes the location of the nodes that represent each item, in pixel coordinates, and associates each item with a particular frame of the logged video. The tool also has functionality to edit existing labeled data, remove or change labels, and to skip through the video file either by single frames, ten frames or to skip to the next labeled frame. The GUI superimposes labeled items onto the logged video during viewing as shown in the screenshot below.