  We address the task of recognizing objects from video input. This important
problem is relatively unexplored, compared with image-based object recognition.
To this end, we make the following contributions. First, we introduce two
comprehensive datasets for video-based object recognition. Second, we propose
Latent Bi-constraint SVM (LBSVM), a maximum-margin framework for video-based
object recognition. LBSVM is based on Structured-Output SVM, but extends it to
handle noisy video data and ensure consistency of the output decision
throughout time. We apply LBSVM to recognize office objects and museum
sculptures, and we demonstrate its benefits over image-based, set-based, and
other video-based object recognition.
