Abstract
In this paper an imaging system for road traffic conflict analysis is proposed. The system exploits geo-referenced stereo sequences and tracking procedure to compute traffic conflict measures which can be analysed by experts. Using the potentiality of the traffic conflict technique as a surrogate safety measure could constitute an effective tool in understanding how the driver interacts and adapts its behaviour with respect to the vehicle, the road characteristics, the traffic control devices and environment. Experiments performed on real data acquired in urban environment confirm the effectiveness of the system which makes simple and fast for the experts the understanding of the driver behaviour.
Chapter PDF
Similar content being viewed by others
References
Chin, H.C., Quek, S.T.: Measurement of Traffic Conflicts. Safety Science 26(3), 169–187 (1997)
Migletz, D.J., Glauz, W.D., Bauer, K.M.: Relationships between Traffic Conflicts and Accidents. Report No: FHWA/RD-84/042. US Department of Transportation, Federal Highway Administration (1985)
Heinrich, H.W.: Industrial Accident Prevention. McGraw-Hill, New York (1932)
Hyden, C.: The Development of Method for Traffic Safety Evaluation: The Swedish Traffic Conflict Technique. Bulletin 70. Lund Ins. of Technology, Sweden (1987)
Songchitruksa, P., Tarko, A.P.: Extreme value theory approach to safety estimation. Accident Analysis & Prevention 38, 811–822 (2006)
Cafiso, S., Garcia, A.G., Cavarra, R., Romero Rojas, M.A.: Crosswalk safety evaluation using a pedestrian risk index as traffic conflict measure. In: The 3rd International Conference on Road safety and Simulation (2011)
Woodfill, J.I., Gordon, G., Buck, R.: Tyzx DeepSea High Speed Stereo Vision System. In: IEEE Workshop on Real Time 3-D Sensors and their Use, IEEE Conference on Computer Vision and Pattern Recognition (2004)
Zabih, R., Woodfill, R.: Non-parametric Local Transforms for Computing Visual Correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, Springer, Heidelberg (1994)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-Based Object Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5) (2003)
Kalal, Z., Mikolajczyk, K., Matas, J.: Face-TLD: Tracking-Learning-Detection applied to faces. In: IEEE International Conference on Image Processing (2010)
Cafiso, S., Di Graziano, A.: Automated in-vehicle data collection and treatment for existing roadway alignment. In: Efficient Transportation and Pavement Systems: Characterization, Mechanisms, Simulation, and Modeling - International Gulf Conference on Roads, pp. 785–797 (2008)
Battiato, S., Curti, S., La Cascia, M., Scordato, E., Tortora, M.: Depth Map Generation by Image Classification. SPIE Electronic Imaging (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Battiato, S., Cafiso, S., Di Graziano, A., Farinella, G.M., Giudice, O. (2013). Road Traffic Conflict Analysis from Geo-referenced Stereo Sequences. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-41181-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41180-9
Online ISBN: 978-3-642-41181-6
eBook Packages: Computer ScienceComputer Science (R0)