An Empirical Study of Context in Object Detection - Robotics Institute Carnegie Mellon University

An Empirical Study of Context in Object Detection

Santosh Kumar Divvala, Derek Hoiem, James H. Hays, Alexei A. Efros, and Martial Hebert
Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 1271 - 1278, June, 2009

Abstract

This paper presents an empirical evaluation of the role of context in a contemporary, challenging object detection task -- the PASCAL VOC 2008. Previous experiments with context have mostly been done on home-grown datasets, often with non-standard baselines, making it difficult to isolate the contribution of contextual information. In this work, we present our analysis on a standard dataset, using top-performing local appearance detectors as baseline. We evaluate several different sources of context and ways to utilize it. While we employ many contextual cues that have been used before, we also propose a few novel ones including the use of geographic context and a new approach for using object spatial support.

BibTeX

@conference{Divvala-2009-10228,
author = {Santosh Kumar Divvala and Derek Hoiem and James H. Hays and Alexei A. Efros and Martial Hebert},
title = {An Empirical Study of Context in Object Detection},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {2009},
month = {June},
pages = {1271 - 1278},
keywords = {Context, Object Detection},
}