Abstract
This paper describes a target detection system on transport infrastructures, based on monocular vision. The goal is to detect and track vehicles and pedestrians, dealing with objects variability, different illumination conditions, shadows, occlusions and rotations. A background subtraction method, based on GMM and shadow detection algorithms are proposed to do the segmentation of the image. Finally a feature extraction, optical flow analysis and clustering methods are used for the tracking step. The algorithm requires no object model and prior knowledge and it is robust to illumination changes and shadows.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (1999)
Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letter (2006)
Joshi, A.J., Papanikolopoulos, N.: Learning to detect moving shadows in dynamic environments. IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)
Horprasert, T., Harwood, D., Davis, L.S.: A statistical approach for real-time robust background subtraction and shadow detection. In: Proc. IEEE Int. Conf. Computer Vision FRAME-RATE Workshop (1999)
Salvador, E., Cavallaro, A., Ebrahimi, T.: Cast shadow segmentation using Invariant color features. Computer Vision and Image Understanding (2004)
Cucchiara, R., Grana, C., Piccardi, M., Prati, A., Sirotti, S.: Improving shadow suppression in moving object detection with HSV color information. In: Proceedings of Intelligent Transportation Systems Conference (2001)
Bas, E., Tekalp, M., Salman, F.S.: Automatic vehicle counting from video for traffic flow analysis. In: Proceedings of IEEE Intelligent Vehicles Symposium (2007)
Kanhere, N.K., Pundlik, S.J., Birchfield, S.T.: Vehicle segmentation and tracking from a low-angle off-axis camera. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, CVPR (2005)
Kim, Z.: Real time object tracking based on dynamic feature grouping with background subtraction. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)
Kölsch, M., Turk, M.: Fast 2D hand tracking with flocks of features and multi-cue integration. In: IEEE Workshop on Real-Time Vision for Human-Computer Interaction (2004)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging Understanding Workshop (1981)
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Álvarez, S., Sotelo, M.A., Llorca, D.F., Quintero, R., Marcos, O. (2012). Monocular Vision-Based Target Detection on Dynamic Transport Infrastructures. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2011. EUROCAST 2011. Lecture Notes in Computer Science, vol 6927. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27549-4_74
Download citation
DOI: https://doi.org/10.1007/978-3-642-27549-4_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27548-7
Online ISBN: 978-3-642-27549-4
eBook Packages: Computer ScienceComputer Science (R0)