This paper presents a novel learning method for human action detection in video sequences. The detecting problem is not limited in controlled settings like stationary background or...
We propose an online algorithm based on local sparse representation for robust object tracking. Local image patches of a target object are represented by their sparse codes with a...
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learni...
Carolina Galleguillos, Boris Babenko, Andrew Rabin...
The ability to accurately localize objects in an observed scene is regarded as an important precondition for many practical applications including automatic manufacturing, quality ...
The present paper addresses pedestrian detection using local boosted features that are learned from a small set of training images. Our contribution is to use two boosting steps. T...
Michael Villamizar, Alberto Sanfeliu, Juan Andrade...