Publication

FlowBoost - Appearance Learning from Sparsely Annotated Video

François Fleuret, Karim Ali, David Hasler
2011
Conference Papers

Abstract

We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists in iteratively improving an appearance based model built with a Boosting procedure, and the reconstruction of trajectories corresponding to the motion of multiple targets. We demonstrate the efficiency of our procedure on pedestrian detection in videos and cell detection in microscopy image sequences. In both cases, our method is demonstrated to reduce the labeling requirement by one to two orders of magnitude. We show that in some instances, our method trained with sparse labels on a video sequence is able to outperform a standard learning procedure trained with the fully labeled sequence.

Official source

https://infoscience.epfl.ch/entities/publication/3521c2ab-a43c-40ca-8afd-bbfb41b555e0

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François Fleuret, Karim Ali, David Hasler
2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/3521c2ab-a43c-40ca-8afd-bbfb41b555e0

About this result

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