In Search of a Car - Utilizing a 3D Model with Context for Object Detection
Mikael Nilsson, Håkan Ardö
2014
Abstract
Automatic video analysis of interactions between road users is desired for city and road planning. A first step of such a system is object localization of road users. In this work, we present a method of detecting a specific car in an intersection from a monocular camera image. A camera calibration and segmentation are utilized as inputs by the method in order to detect a car. Using these inputs, a sampled search space in the ground plane, including rotations, is explored with a 3D model of a car in order to produce output in form of rectangle detections in the ground plane. Evaluation on real recorded data, with ground truth for one car using GPS, indicates that a car can be detected in over 90% of the time with an average error around 0.5m.
References
- Ardö, H. and A°ström, K. (2009). Bayesian formulation of image patch matching using cross-correlation. In Third ACM/IEEE International Conference on Distributed Smart Cameras, pages 1-8.
- Ardö, H. and Svärd, L. (2014). Bayesian formulation of gradient orientation matching. Submitted to CVPR 2014.
- Carr, P., Sheikh, Y., and Matthews, I. (2012). Monocular object detection using 3d geometric primitives. In Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C., editors, Computer Vision ECCV 2012, volume 7572 of Lecture Notes in Computer Science, pages 864-878. Springer Berlin Heidelberg.
- Dollár, P., Appel, R., and Kienzle, W. (2012). Crosstalk cascades for frame-rate pedestrian detection. In Proceedings of the 12th European conference on Computer Vision - Volume Part II, ECCV'12, pages 645- 659, Berlin, Heidelberg. Springer-Verlag.
- Felzenszwalb, P., Girshick, R., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(9):1627-1645.
- Ferryman, J., Worrall, A., Sullivan, G., and Baker, K. (1997). Visual surveillance using deformable models of vehicles. Robotics and Autonomous Systems, 19(34):315 - 335.
- Koller, D., Danilidis, K., and Nagel, H.-H. (1993). Modelbased object tracking in monocular image sequences of road traffic scenes. Int. J. Comput. Vision, 10(3):257-281.
- Li, Y., Gu, L., and Kanade, T. (2009). A robust shape model for multi-view car alignment. In The IEEE International Conference on Computer Vision and Pattern Recognition.
- Nilsson, M., Ardö, H., Laureshyn, A., and Persson, A. (2013). Reduced search space for rapid bicycle detection. In International Conference on Pattern Recognition Applications and Methods (ICPRAM).
- Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3):pp. 1065-1076.
- Pepik, B., Stark, M., Gehler, P., and Schiele, B. (2012). Teaching 3d geometry to deformable part models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012, Providence, RI, USA. accepted as oral.
- Song, X. and Nevatia, R. (2007). Detection and tracking of moving vehicles in crowded scenes. In Motion and Video Computing, 2007. WMVC 7807. IEEE Workshop on, pages 4-4.
- Tan, T. N., Sullivan, G. D., and Baker, K. D. (1998). Modelbased localisation and recognition of road vehicles. Int. J. Comput. Vision, 27(1):5-25.
- Tsai, R. (1987). A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf tv cameras and lenses. Robotics and Automation, IEEE Journal of, 3(4):323-344.
- Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages 511-518.
Paper Citation
in Harvard Style
Nilsson M. and Ardö H. (2014). In Search of a Car - Utilizing a 3D Model with Context for Object Detection . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 419-424. DOI: 10.5220/0004685304190424
in Bibtex Style
@conference{visapp14,
author={Mikael Nilsson and Håkan Ardö},
title={In Search of a Car - Utilizing a 3D Model with Context for Object Detection},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={419-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004685304190424},
isbn={978-989-758-004-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - In Search of a Car - Utilizing a 3D Model with Context for Object Detection
SN - 978-989-758-004-8
AU - Nilsson M.
AU - Ardö H.
PY - 2014
SP - 419
EP - 424
DO - 10.5220/0004685304190424