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Monocular Vision-Based Target Detection on Dynamic Transport Infrastructures

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Book cover Computer Aided Systems Theory – EUROCAST 2011 (EUROCAST 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6927))

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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.

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References

  1. 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)

    Google Scholar 

  2. Zivkovic, Z., van der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letter (2006)

    Google Scholar 

  3. Joshi, A.J., Papanikolopoulos, N.: Learning to detect moving shadows in dynamic environments. IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. Salvador, E., Cavallaro, A., Ebrahimi, T.: Cast shadow segmentation using Invariant color features. Computer Vision and Image Understanding (2004)

    Google Scholar 

  6. 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)

    Google Scholar 

  7. Bas, E., Tekalp, M., Salman, F.S.: Automatic vehicle counting from video for traffic flow analysis. In: Proceedings of IEEE Intelligent Vehicles Symposium (2007)

    Google Scholar 

  8. 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)

    Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    Google Scholar 

  11. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging Understanding Workshop (1981)

    Google Scholar 

  12. Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR (1994)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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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

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  • 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)

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