Pedestrian tracking from a moving vehicle

Intelligent vehicles and unattended driving systems of the future will need the ability to recognize relevant traffic participants (such as other vehicles, pedestrians, bicyclists, etc.) and detect dangerous situations ahead of time. An important component of these systems is one that is able to distinguish pedestrians and track their motion to make intelligent driving decisions. The associated computer vision problem that needs to be solved is detection and tracking of pedestrians from a moving camera, which is extremely challenging. Robust pedestrian tracking performance can be achieved by temporal integration of the data in a probabilistic setting. We employ a shape model for pedestrians and an efficient variant of the condensation tracker to achieve these objectives. The tracking is performed in the high-dimensional space of shape model parameters which consists of Euclidean transformation parameters and deformation parameters. Our condensation tracker employs sampling on quasi-random points, improving its asymptotic complexity and robustness, and making it amenable to real-time implementation.

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