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An Empirical Study of Context in Object Detection

Santosh Kumar Divvala, Derek Hoiem, James H. Hays, Alexei A. Efros and Martial Hebert
Carnegie Mellon University, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2009

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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 Reference
@conference{Divvala-2009-10228,
title = {An Empirical Study of Context in Object Detection},
author = {Santosh Kumar Divvala and Derek Hoiem and James H. Hays and Alexei A. Efros and Martial Hebert},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
keyword = {Context, Object Detection},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2009},
address = {Pittsburgh, PA},
}
2017-09-13T10:41:10+00:00