Abstract
This research studies motion segmentation based on dense optical flow fields for mobile robotic applications. The optical flow is usually represented in the Euclidean space however, finding the most suitable motion space is a relevant problem because techniques for motion analysis have distinct performances. Factors like the processing-time and the quality of the segmentation provide a quantitative evaluation of the clustering process. Therefore, this paper defines a methodology that evaluates and compares the advantage of clustering dense flow fields using different feature spaces, for instance, Euclidean and Polar space. The methodology resorts to conventional clustering techniques, Expectation-Maximization and K-means, as baseline methods. The experiments conducted during this paper proved that the K-means clustering is suitable for analyzing dense flow fields.
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
Brox, T., Malik, J.: Object segmentation by long term analysis of point trajectories. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 282–295. Springer, Heidelberg (2010)
Bugeau, A., Prez, P.: Detection and segmentation of moving objects in complex scenes. Computer Vision and Image Understanding 113(4), 459–476 (2009)
Eibl, G., Brandle, N.: Evaluation of clustering methods for finding dominant optical flow fields in crowded scenes. In: International Conference on Pattern Recognition, pp. 1–4 (December 2008)
Georgiadis, G., Ayvaci, A., Soatto, S.: Actionable saliency detection: Independent motion detection without independent motion estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 646–653 (2012)
Pinto, A.M., Costa, P.G., Correia, M.V., Paulo Moreira, A.: Enhancing dynamic videos for surveillance and robotic applications: The robust bilateral and temporal filter. Signal Processing: Image Communication 29(1), 80–95 (2014)
Dan Melamed, I., Green, R., Turian, J.P.: Precision and recall of machine translation. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Companion Volume of the Proceedings of HLT-NAACL, NAACL-Short 2003, pp. 61–63. Association for Computational Linguistics, Stroudsburg (2003)
Pinto, A.M., Paulo Moreira, A., Correia, M.V., Costa, P.G.: A flow-based motion perception technique for an autonomous robot system. Journal of Intelligent and Robotic Systems, 1–25 (2013) (in press)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Pinto, A., Costa, P., Moreira, A.P. (2015). Detecting Motion Patterns in Dense Flow Fields: Euclidean Versus Polar Space. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-23485-4_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23484-7
Online ISBN: 978-3-319-23485-4
eBook Packages: Computer ScienceComputer Science (R0)