1-D Temporal Segments Analysis for Traffic Video Surveillance
M. Brulin, C. Maillet, H. Nicolas
2014
Abstract
Traffic video surveillance is an important topic for security purposes and to improve the traffic flow management. Video surveillance can be used for different purposes such as counting of vehicles or to detect their speed and behaviors. In this context, it is often important to be able to analyze the video in real-time. The huge amount of data generated by the increasing number of cameras is an obstacle to reach this goal. A solution consists in selecting in the video only the regions of interest, essentially the vehicles on the road areas. In this paper, we propose to extract significant segments of the regions of interest and to analyze them temporally to count vehicles and to define their behaviors. Experiments on real data show that precise vehicle’s counting and high recall and precision are obtain for vehicle’s behavior and traffic analysis.
References
- V. Kastrina, M. Zervakis and K. Kalaitzakis. A survey of video processing techniques for traffic applications. Image and Vision Computing, Vol. 21, N°4, pp. 359- 381, 2003.
- N. Buch, S. A. Velastin and J. Orwell. A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation system, Vol. 12, N°3, pp. 920-939, 2011.
- B. Tian, Q. Yao, Y. Gu, K. Wang and Y. Li. Video processing techniques for traffic flow monitoring: A survey. In Proc. of Int. Conf. on Intelligent Transportation Systems, pp. 1103-1108, 2011.
- Z. Zhu, G. Xu, B. Yang, D. Shi and X. Lin. VISATRAM: A real-time vision system for automatic traffic monitoring. Image and Vision Computing, Vol. 18, No. 10, pp.781-794, 2000.
- A. Yoneyama, C. H. Yeh and C. C. J. Kuo. Robust vehicle and traffic information extraction for highway surveillance. Image and Vision Computing, Vol. 2005, pp. 2305-2321, 2005.
- T. Rodriguez and N. Garcia. An adaptive, real-time, traffic monitoring system. Machine Vision and Applications. Vol. 21, No. 4, pp. 555-576, 2010.
- A. Bissacco, P. Saisan and S. Soatto. Gait recognition using dynamic affine invariant. In int. Symposium on Mathematical Theory of Network, and Systems. 2004.
- A. Adam, E. rivlin, I. Shimshoni and D. Reinitz. Robust real-time unusual event detection using multiple fixedlocation monitors. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 30, N°3, pp.555-560, 2008.
- C. Stauffer. Estimating tracking sources and sinks. In Proc. of Computer Vision and Pattern Recognition, IEEE, Vol. 4, pp.35-45, 2003.
- Y. Malinovski, Y. Wang and Y.J. Wu. Video-based vehicle detection and tracking using spatio-temporal maps. Proc. of the Annual Transportation Research Board meeting, Washington DC, 2009.
- Bouwmans, T., El Baf, F. and Vachon, B., Background modeling using mixture of gaussians for foreground detection - A survey. Recent Patents on Computer Science, pp. 219-237, 2008.
Paper Citation
in Harvard Style
Brulin M., Maillet C. and Nicolas H. (2014). 1-D Temporal Segments Analysis for Traffic Video Surveillance . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 557-563. DOI: 10.5220/0004733905570563
in Bibtex Style
@conference{visapp14,
author={M. Brulin and C. Maillet and H. Nicolas},
title={1-D Temporal Segments Analysis for Traffic Video Surveillance},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={557-563},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004733905570563},
isbn={978-989-758-003-1},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - 1-D Temporal Segments Analysis for Traffic Video Surveillance
SN - 978-989-758-003-1
AU - Brulin M.
AU - Maillet C.
AU - Nicolas H.
PY - 2014
SP - 557
EP - 563
DO - 10.5220/0004733905570563