FlowBoost - Appearance Learning from Sparsely Annotated Video

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.


Published in:
2011 Ieee Conference On Computer Vision And Pattern Recognition (Cvpr), 1433-1440
Presented at:
IEEE International Conference on Computer Vision and Pattern Recognition
Year:
2011
Publisher:
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
ISBN:
978-1-4577-0393-5
Keywords:
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 Record created 2011-03-04, last modified 2018-03-18

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