A final evaluation of pedestrian detection and tracking from a moving platform

Bodt, Barry A.; Camden, Richard

doi:10.1117/12.850490

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ARL anticipates publishing results from the RCTA capstone experiment at NIST later this year [19] [20]. ARL plans on publishing results from an earlier experiment at NIST (January 2009) in the proceedings of this conference [21].…”

Section: Discussionmentioning

confidence: 98%

Stereo-vision-based perception capabilities developed during the Robotics Collaborative Technology Alliances program

et al. 2010

View full text Add to dashboard Cite

The Robotics Collaborative Technology Alliances (RCTA) program, which ran from 2001 to 2009, was funded by the U.S. Army Research Laboratory and managed by General Dynamics Robotic Systems. The alliance brought together a team of government, industrial, and academic institutions to address research and development required to enable the deployment of future military unmanned ground vehicle systems ranging in size from man-portables to ground combat vehicles. Under RCTA, three technology areas critical to the development of future autonomous unmanned systems were addressed: advanced perception, intelligent control architectures and tactical behaviors, and human-robot interaction. The Jet Propulsion Laboratory (JPL) participated as a member for the entire program, working four tasks in the advanced perception technology area: stereo improvements, terrain classification, pedestrian detection in dynamic environments, and long range terrain classification. Under the stereo task, significant improvements were made to the quality of stereo range data used as a front end to the other three tasks. Under the terrain classification task, a multi-cue water detector was developed that fuses cues from color, texture, and stereo range data, and three standalone water detectors were developed based on sky reflections, object reflections (such as trees), and color variation. In addition, a multi-sensor mud detector was developed that fuses cues from color stereo and polarization sensors. Under the long range terrain classification task, a classifier was implemented that uses unsupervised and self-supervised learning of traversability to extend the classification of terrain over which the vehicle drives to the far-field. Under the pedestrian detection task, stereo vision was used to identify regions-of-interest in an image, classify those regions based on shape, and track detected pedestrians in three-dimensional world coordinates. To improve the detectability of partially occluded pedestrians and reduce pedestrian false alarms, a vehicle detection algorithm was developed. This paper summarizes JPL's stereo-vision based perception contributions to the RCTA program.

show abstract

Section: Discussionmentioning

confidence: 98%