Learning Based System for Detection and Tracking of Vehicles

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.

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