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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.

Keywords

Context, Object Detection

Notes

Text Reference

Santosh Kumar Divvala, Derek Hoiem, James H. Hays, Alexei A. Efros, and Martial Hebert, "An Empirical Study of Context in Object Detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2009.

BibTeX Reference

@inproceedings{Divvala_2009_6350,
   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 = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2009",
}

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