01367631

Component

This paper describes an automated classification approach of road-users. The classification relies on video data (movement trajectories) collected in urban intersections. The approach is based on the discrimination between the movement speed profiles of each road-user type. More precisely, the discrimination between the speed movement patterns of vehicles and the ambulatory characteristics of pedestrians. The collected movement trajectories data are represented as time-series. The classification is performed using the singular value decomposition and reconstruction of the time-series. Two complementary methods are proposed based on the quality evaluation (correlation score) of the reconstructed trajectories. In the first method, a threshold based decision procedure is applied. This approach is complemented in the second method by a semi-supervised classification procedure guided by movement prototypes. The approach is validated on real world data collected in Oakland, California. A correct classification of around 90% was achieved using both methods. The main benefit of this research is to apply classification as a first step in the activity and behavior recognition of pedestrians in traffic scenes.

01362476

12-1744

English

20p

2012

Transportation Research Board 91st Annual Meeting

Digital/other

Figures; References

Automatic vehicle classification; Behavior; Highway safety; Pedestrian movement; Time series; Vehicle trajectories

Oakland (California)

Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors; I72: Traffic and Transport Planning; I83: Accidents and the Human Factor

Transportation Research Board Annual Meeting 2012 Paper #12-1744

TRIS, TRB

Feb 8 2012 5:05PM