Latent Bi-constraint SVM for Video-based Object Recognition

TL;DR

This paper introduces a new framework called Latent Bi-constraint SVM for recognizing objects in videos, addressing the challenges of noisy data and temporal consistency, and provides new datasets for this task.

Contribution

The paper presents a novel maximum-margin framework, LBSVM, for video object recognition, extending Structured-Output SVM to handle temporal data and noise.

Findings

LBSVM outperforms existing image-based and video-based methods.

New datasets for video-based object recognition are introduced.

LBSVM demonstrates improved recognition accuracy on office objects and sculptures.

Abstract

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.