Abstract
To enable the precise detection of persons walking or running on the ground using unmanned Micro Aerial Vehicles (MAVs), we present the evaluation of the MCT algorithm based on intensity as well as gradient images for optical flow, focusing on accuracy as well as low computational complexity to enable the real-time implementation in light-weight embedded systems. Therefore, we give a detailed analysis of this algorithm on four optical flow datasets from the Middlebury database and show the algorithm’s performance when compared to other optical flow algorithms. Furthermore, different approaches for sub-pixel refinement and occlusion detection are discussed.
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.
References
Ambrosch, K.: Mapping Stereo Matching Algorithms to Hardware. PhD thesis, Vienna University of Technology (2009)
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging Understanding Workshop, pp. 121–130 (1981)
Horn, B., Schunck, B.: Determining optical flow. MIT - Artificial Intelligence Memo No.572 (1980)
Ogale, A., Aloimonos, Y.: A roadmap to the integration of early visual modules. International Journal of Computer Vision: Special Issue on Early Cognitive Vision 72 (2007)
Giachetti, A.: Matching techniques to compute image motion. Image and Vision Computing 18 (2000)
Claus, C., Laika, L., Jia, L., Stechele, W.: High performance fpga based optical flow calculation using the census transformation. In: The Intelligent Vehicles Symposium (2009)
Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, pp. 151–158. Springer, Heidelberg (1994)
Barron-Zambrano, J., Torres-Huitzil, C., Cerda, M.: Flexible architecture for three classes of optical flow extraction algorithms. In: International Conference on Reconfigurable Computing and FPGAs, pp. 13–18 (2008)
Fukuoka, Y., Shibata, T.: Block-matching-based cmos optical flow sensor using only-nearest-neighbor computation. In: IEEE International Symposium on Circuits and Systems, pp. 1485–1488 (2009)
Sadykhov, R.K., Lamovsky, D.V.: Fast cross correlation algorithm for optical flow estimation. In: Proceedings of the 7th Nordic Signal Processing Symposium, pp. 322–325 (2006)
Froeba, B., Ernst, A.: Face detection with the modified census transform. In: Proceedings of the Sixth IEEE Conference on Automatic Face and Gesture Recognition (2004)
Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision (IJCV) 57
Krsek, P., Pajdla, T., Hlaváč, V.: Estimation of differential structures on triangulated surfaces. In: 21st Workshop of the Austrian Association for Pattern Recognition (1997)
Shimizu, M., Okutomi, M.: Precise sub-pixel estimation on area-based matching. In: Proceedings of the Eight IEEE International Conference on Computer Vision (2003)
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. In: Proceedings of the Eleventh IEEE Conference on Computer Vision (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Puxbaum, P., Ambrosch, K. (2010). Gradient-Based Modified Census Transform for Optical Flow. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-17289-2_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17288-5
Online ISBN: 978-3-642-17289-2
eBook Packages: Computer ScienceComputer Science (R0)