Vehicle Detection with Context

Yang Hu, Larry S. Davis

2013

Abstract

Detecting vehicles in satellite images has a wide range of applications. Existing approaches usually identify vehicles from their appearance. They typically generate many false positives due to the existence of a large number of structures that resemble vehicles in the images. In this paper, we explore the use of context information to improve vehicle detection performance. In particular, we use shadows and the ground appearance around vehicles as context clues to validate putative detections. A data driven approach is applied to learn typical patterns of vehicle shadows and the surrounding “road-like” areas. By observing that vehicles often appear in parallel groups in urban areas, we also use the orientations of nearby detections as another context clue. A conditional random field (CRF) is employed to systematically model and integrate these different contextual knowledge. We present results on two sets of images from Google Earth. The proposed method significantly improves the performance of the base appearance based vehicle detector. It also outperforms another state-of-the-art context model.

References

  1. Chellappa, R., Zheng, Q., Davis, L., Lin, C., Zhang, X., Rodriguez, C., Rosenfeld, A., and Moore, T. (1994). Site model based monitoring of aerial images. In Image Understanding Workshop.
  2. Choi, J.-Y. and Yang, Y.-K. (2009). Vehicle detection from aerial images using local shape information. In Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology.
  3. Comaniciu, D. and Meer, P. (2002). Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603-619.
  4. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Proceedings of the 18th IEEE Conference on Computer Vision and Pattern Recognition.
  5. Divvala, S., Hoiem, D., Hays, J., Efros, A., and Hebert, M. (2009). An empirical study of context in object detection. In Proceedings of the 22th IEEE Conference on Computer Vision and Pattern Recognition.
  6. Grabner, H., Nguyen, T. T., Gruber, B., and Bischof, H. (2008). On-line boosting-based car detection from aerial images. ISPRS Journal of Photogrammetry and Remote Sensing, 63(3):382-396.
  7. Guo, R., Dai, Q., and Hoiem, D. (2011). Single-image shadow detection and removal using paired regions. In Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition.
  8. Heitz, G. and Koller, D. (2008). Learning spatial context: using stuff to find things. In Proceedings of the 10th European Conference on Computer Vision.
  9. Hinz, S. and Baumgartner, A. (2001). Vehicle detection in aerial images using generic features, grouping, and context. In Proceedings of the 23rd DAGMSymposium on Pattern Recognition.
  10. Jin, X. and Davis, C. H. (2007). Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks. Image and Vision Computing, 25(9):1422-1431.
  11. Kembhavi, A., Harwood, D., and Davis, L. S. (2011). Vehicle detection using partial least squares. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(6):1250-1265.
  12. Marszalek, M., Laptev, I., and Schmid, C. (2009). Actions in context. In Proceedings of the 22th IEEE Conference on Computer Vision and Pattern Recognition.
  13. Moon, H., Chellappa, R., and Rosenfeld, A. (2002). Optimal edge-based shape detection. IEEE Transactions on In Image Processing, 11(11):1209-1227.
  14. Murphy, K., Torralba, A., and Freeman, W. (2003). Using the forest to see the trees: a graphical model relating features, objects, and scenes. In Advances in Neural Information Processing Systems.
  15. Oliva, A. and Torralba, A. (2007). The role of context in object recognition. Trends in Cognitive Sciences, 11(12):520-527.
  16. Quint, F. (1997). MOSES: a structural approach to aerial image understanding. Automatic Extraction of Manmade Objects from Aerial and Space Images (II), pages 323-332.
  17. Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., and Belongie, S. (2007). Objects in context. In Proceedings of the International Conference on Computer Vision.
  18. Schwartz, W. R., Kembhavi, A., Harwood, D., and Davis, L. S. (2009). Human detection using partial least squares analysis. In Proceedings of the 12th International Conference on Computer Vision.
  19. Yao, B. and Fei-Fei, L. (2012). Recognizing human-object interactions in still images by modeling the mutual context of objects and human poses. IEEE Transactions on Pattern Analysis and Machine Intelligence.
  20. Zhao, T. and Nevatia, R. (2003). Car detection in low resolution aerial images. Image and Vision Computing, 21(8):693-703.
  21. Zhu, J., Samuel, K. G. G., Masood, S. Z., and Tappen, M. F. (2010). Learning to recognize shadows in monochromatic natural images. In Proceedings of the 23th IEEE Conference on Computer Vision and Pattern Recognition.
  22. Zhu, Q., Avidan, S., Yeh, M.-C., and Cheng, K.-T. (2006). Fast human detection using a cascade of histograms of oriented gradients. In Proceedings of the 19th IEEE Conference on Computer Vision and Pattern Recognition.
Download


Paper Citation


in Harvard Style

Hu Y. and S. Davis L. (2013). Vehicle Detection with Context . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 715-722. DOI: 10.5220/0004302907150722


in Bibtex Style

@conference{visapp13,
author={Yang Hu and Larry S. Davis},
title={Vehicle Detection with Context},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={715-722},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004302907150722},
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 - Vehicle Detection with Context
SN - 978-989-8565-47-1
AU - Hu Y.
AU - S. Davis L.
PY - 2013
SP - 715
EP - 722
DO - 10.5220/0004302907150722