Object’s scale and rotation estimation using the clustering in tracking methods based on the optic flow calculation

Main Article Content

Anton Yuriiovych Varfolomeev
Oleksandr Mykolaiovych Lysenko

Abstract

Procedures of scale and rotation estimation of an object using the clustering are proposed. The pro-cedures are oriented to the application in tracking techniques based on sparse optic flow calculation. The new tracking method, which is called FCT (Flow Clustering Tracker) is developed using the mentioned procedures. The obtained results show its ability to achieve the compromise in robustness and accuracy with respect to the existing solutions.

References 8, figures 5, tables. 2.

Article Details

How to Cite
Varfolomeev, A. Y., & Lysenko, O. M. (2016). Object’s scale and rotation estimation using the clustering in tracking methods based on the optic flow calculation. Electronics and Communications, 21(2), 32–40. https://doi.org/10.20535/2312-1807.2016.21.2.69771
Section
Methods and means of processing signals and images

References

Forsyth, D., & Ponce, J. (2004). Computer vision: A modern approach. Moscow: Williams Publishing House. (Rus)

Boguet, J.-Y. (2002). Pyramidal Implementation of the Lucas-Kanade Feature Tracker: Description of the algorithm. (Tech. report, Intel Corporation. Microprocessor Research Labs).

Bradski, G., & Kaehler, A. (2008). Learning OpenCV. Sebastopol (CA): O'Reilly.

Kalal, Z., Mikolajczyk, K., & Matas, J. (2010). Forward-Backward Error: Automatic Detection of Tracking Failures. 2010 20th International Conference on Pattern Recognition, 2756-2759. doi:10.1109/icpr.2010.675.

Maresca, M.E., & Petrosino, A. (2015). Clustering Local Motion Estimates for Robust and Efficient Object Tracking. Computer Vision - ECCV 2014 Workshops Lecture Notes in Computer Science, 244-253. doi:10.1007/978-3-319-16181-5_17.

Rosten, E., & Drummond, T. (2006). Machine Learning for High-Speed Corner Detection. Computer Vision – ECCV 2006 Lecture Notes in Computer Science, 430-443. doi:10.1007/11744023_34.

Kristan, M., Matas, J., Leonardis, A., Felsberg, M., Cehovin, L., Fernandez, G., … Pflugfelder, R. (2015). The Visual Object Tracking VOT2015 Challenge Results. 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). doi:10.1109/iccvw.2015.79.

Vojíř, T. & Matas, J. (2012). Increasing Robustness of the Flock of Trackers (Research Report of CMP No. 14., Czech Technical University in Prague).