AUTOMATIC ESTIMATION OF MULTIPLE MOTION FIELDS USING OBJECT TRAJECTORIES AND OPTICAL FLOW
Manya V. Afonso, Jorge S. Marques, Jacinto C. Nascimento
2012
Abstract
Multiple motion fields are an efficient way of summarising the movement of objects in a scene and allow an automatic classification of objects activities in the scene. However, their estimation relies on some kind of supervised learning e.g., using manually edited trajectories. This paper proposes an automatic method for the estimation of multiple motion fields. The proposed algorithm detects multiple moving objects and their velocities in a video sequence using optical flow. This leads to a sequence of centroids and corresponding velocity vectors. A matching algorithm is then applied to group the centroids into trajectories, each of them describing the movement of an object in the scene. The paper shows that motion fields can be reliably estimated from the detected trajectories leading to a fully automatic procedure for the estimation of multiple motion fields.
References
- Barron, J. L., Fleet, D. J., and Beauchemin, S. S. (1994). Performance of optical flow techniques. International Journal of Computer Vision, 12:43-77.
- Brox, T., Bruhn, A., Papenberg, N., and Weickert, J. (2004). High accuracy optical flow estimation based on a theory for warping. In ECCV (4), pages 25-36.
- Collins, R., Lipton, A., and Kanade, T. (1999). A system for video surveillance and monitoring. In Proc. American Nuclear Society (ANS) Eighth Int. Topical Meeting on Robotic and Remote Systems, pages 25- 29, Pittsburgh, PA.
- Gonzalez, R. C. and Woods, R. E. (2002). Digital image processing. Prentice Hall.
- Haritaoglu, I., Harwood, D., and Davis, L. S. (2000). W 4: real-time surveillance of people and their activities. 22(8):809-830.
- Horn, B. K. P. and Schunck, B. G. (1981). Determining optical flow. Artificial Intelligence, 17:185-203.
- Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B., and Russel, S. (1994). Towards robust automatic traffic scene analysis in real-time. In Proc. of Int. Conf. on Pat. Rec., pages 126-131.
- Kuhn, H. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97.
- Lucas, B. D. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. pages 674-679.
- Ma, Y.-F. and Zhang, H.-J. (2001). Detecting motion object by spatio-temporal entropy. In IEEE Int. Conf. on Multimedia and Expo, Tokyo, Japan.
- McKenna, S. J. and Gong, S. (1999). Tracking colour objects using adaptive mixture models. Image Vision Computing, 17:225-231.
- Nascimento, J., Figueiredo, M., and Marques, J. (2009). Trajectory analysis in natural images using mixtures of vector fields. In IEEE Int. Conf. on Image Proc., pages 4353 -4356.
- Nascimento, J., Figueiredo, M., and Marques, J. (2010). Trajectory classification using switched dynamical hidden markov models. IEEE Trans. on Image Proc., 19(5):1338 -1348.
- Ohta, N. (2001). A statistical approach to background suppression for surveillance systems. In Proc. of IEEE Int. Conf. on Computer Vision, pages 481-486.
- Sand, P. and Teller, S. (2008). Particle video: Long-range motion estimation using point trajectories. Int. J. Comput. Vision, 80:72-91.
- Simoncelli, E. P. (1993). Course-to-fine estimation of visual motion. In IEEE Eighth Workshop on Image and Multidimensional Signal Processing.
- Souvenir, R., Wright, J., and Pless, R. (2005). Spatiotemporal detection and isolation: Results on the PETS2005 datasets. In Proceedings of the IEEE Workshop on Performance Evaluation in Tracking and Surveillance.
- Stauffer, C., Eric, W., and Grimson, L. (2000). Learning patterns of activity using real-time tracking. 22(8):747-757.
- Turaga, P., Chellappa, R., Subrahmanian, V., and Udrea, O. (2008). Machine recognition of human activities: A survey. Circuits and Systems for Video Technology, IEEE Transactions on, 18(11):1473 -1488.
- Veenman, C., Reinders, M., and Backer, E. (2001). Resolving motion correspondence for densely moving points. IEEE Trans. on Pattern Analysis and Machine Intelligence, 23(1):54 -72.
- Wren, C. R., Azarbayejani, A., Darrell, T., and Pentland, A. P. (1997). Pfinder: Real-time tracking of the human body. 19(7):780-785.
Paper Citation
in Harvard Style
Afonso M., Marques J. and Nascimento J. (2012). AUTOMATIC ESTIMATION OF MULTIPLE MOTION FIELDS USING OBJECT TRAJECTORIES AND OPTICAL FLOW . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 457-462. DOI: 10.5220/0003783404570462
in Bibtex Style
@conference{icpram12,
author={Manya V. Afonso and Jorge S. Marques and Jacinto C. Nascimento},
title={AUTOMATIC ESTIMATION OF MULTIPLE MOTION FIELDS USING OBJECT TRAJECTORIES AND OPTICAL FLOW},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={457-462},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003783404570462},
isbn={978-989-8425-99-7},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - AUTOMATIC ESTIMATION OF MULTIPLE MOTION FIELDS USING OBJECT TRAJECTORIES AND OPTICAL FLOW
SN - 978-989-8425-99-7
AU - Afonso M.
AU - Marques J.
AU - Nascimento J.
PY - 2012
SP - 457
EP - 462
DO - 10.5220/0003783404570462