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Discriminatively Trained Particle Filters for Complex Multi-Object Tracking

TitleDiscriminatively Trained Particle Filters for Complex Multi-Object Tracking
Publication TypeConference Paper
Year of Publication2009
AuthorsHess, R., and A. Fern
Conference Name2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops)
Pagination240 - 247
Date Published06/2009
Conference LocationMiami, FL
ISBN Number978-1-4244-3992-8

This work presents a discriminative training method for particle filters in the context of multi-object tracking. We are motivated by the difficulty of hand-tuning the many model parameters for such applications and also by results in many application domains indicating that discriminative training is often superior to generative training methods. Our learning approach is tightly integrated into the actual inference process of the filter and attempts to directly optimize the filter parameters in response to observed errors. We present experimental results in the challenging domain of American football where our filter is trained to track all 22 players throughout football plays. The training method is shown to significantly improve performance of the tracker and to significantly outperform two recent particle-based multi-object tracking methods.