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

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
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).