A Region Driven and Contextualized Pedestrian Detector

Thierry Chesnais, Thierry Chateau, Nicolas Allezard, Yoann Dhome, Boris Meden, Mohamed Tamaazousti, Adrien Chan-Hon-Tong

2013

Abstract

This paper tackles the real-time pedestrian detection problem using a stationary calibrated camera. Problems frequently encountered are: a generic classifier can not be adjusted to each situation and the perspective deformations of the camera can profoundly change the appearance of a person. To avoid these drawbacks we contextualized a detector with information coming directly from the scene. Our method comprises three distinct parts. First an oracle gathers examples from the scene. Then, the scene is split in different regions and one classifier is trained for each one. Finally each detector are automatically tuned to achieve the best performances. Designed for making camera network installation procedure easier, our method is completely automatic and does not need any knowledge about the scene.

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S. (2012). SLIC Superpixels Compared to State-of-the-art Superpixel Methods. PAMI.
  2. Dalal, N. and Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. In CVPR.
  3. Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In CVPR.
  4. Grabner, H., Roth, P. M., and Bischof, H. (2007). Is pedestrian detection really a hard task? In PETS.
  5. Park, D., Ramanan, D., and Fowlkes, C. (2010). Multiresolution models for object detection. In ECCV.
  6. Rodriguez, M., Sivic, J., Laptev, I., and Audibert, J.-Y. (2011). Density-aware person detection and tracking in crowds. In ICCV.
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Paper Citation


in Harvard Style

Chesnais T., Chateau T., Allezard N., Dhome Y., Meden B., Tamaazousti M. and Chan-Hon-Tong A. (2013). A Region Driven and Contextualized Pedestrian Detector . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 796-799. DOI: 10.5220/0004292607960799


in Bibtex Style

@conference{visapp13,
author={Thierry Chesnais and Thierry Chateau and Nicolas Allezard and Yoann Dhome and Boris Meden and Mohamed Tamaazousti and Adrien Chan-Hon-Tong},
title={A Region Driven and Contextualized Pedestrian Detector},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={796-799},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004292607960799},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - A Region Driven and Contextualized Pedestrian Detector
SN - 978-989-8565-47-1
AU - Chesnais T.
AU - Chateau T.
AU - Allezard N.
AU - Dhome Y.
AU - Meden B.
AU - Tamaazousti M.
AU - Chan-Hon-Tong A.
PY - 2013
SP - 796
EP - 799
DO - 10.5220/0004292607960799