Learning based system for detection and tracking of vehicles

Authors:
Hakan Ardo
Affiliations:
Center for Mathematical Sciences, Lund University, Sweden
Venue:
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Year:
2005

Citing 5
Cited 0

Learning the distribution of object trajectories for event recognition

BMVC '95 Proceedings of the 6th British conference on Machine vision (Vol. 2)
Combination of Edge Element and Optical Flow Estimates for 3D-Model-Based Vehicle Tracking in Traffic Image Sequences

International Journal of Computer Vision
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Using Adaptive Tracking to Classify and Monitor Activities in a Site

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Statistical Background Subtraction for a Mobile Observer

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we study how learning can be used in several aspects of car detection and tracking. The overall goal is to develop a system that learns its surrounding and subsequently does a good job in detecting and tracking all cars (and later pedestrians and bicycles) in an intersection. Such data can then be analyzed in order to determine how safe an intersection is. The system is designed to, with minimal supervision, learn the location of the roads, the geometry needed for rectification, the size of the vehicles and the tracks used to pass the intersection. Several steps in the tracking process are described. The system is verified with experimental data, with promising results.