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 throughout the video sequence. We demonstrate the efficiency of such a procedure on both pedestrian detection in videos and cell detection in microscopic images. In both cases, our method is demonstrated to reduce the labeling requirement by one to two orders of magnitude. Surprisingly, we show that in some cases, our method trained with sparse labels on a video sequence is able to outperform a standard learning procedure trained with the fully labeled sequence.