Mean shift object tracking using a 4d kernel and linear prediction

Conference contribution
(Conference Contribution)


Publication Details

Author(s): Quast K, Kobylko C, Kaup A
Publication year: 2011
Pages range: 588-593
ISBN: 9789898425478
Language: English


Abstract


A new mean shift tracker which tracks not only the position but also the size and orientation of an object is presented. By using a four-dimensional kernel, the mean shift iterations are performed in a four-dimensional search space consisting of the image coordinates, a scale and an orientation dimension. Thus, the enhanced mean shift tracker tracks the position, size and orientation of an object simultaneously. To increase the tracking performance by using the information about the position, size and orientation of the object in the previous frames, a linear prediction is also integrated into the 4D kernel tracker. The tracking performance is further improved by considering the gradient norm as an additional object feature.


FAU Authors / FAU Editors

Kaup, André Prof. Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung
Quast, Katharina Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung


How to cite

APA:
Quast, K., Kobylko, C., & Kaup, A. (2011). Mean shift object tracking using a 4d kernel and linear prediction. In Proceedings of the International Conference on Computer Vision Theory and Application, VISAPP 2011 (pp. 588-593). Vilamoura, Algarve, PT.

MLA:
Quast, Katharina, C. Kobylko, and André Kaup. "Mean shift object tracking using a 4d kernel and linear prediction." Proceedings of the International Conference on Computer Vision Theory and Application, VISAPP 2011, Vilamoura, Algarve 2011. 588-593.

BibTeX: 

Last updated on 2019-28-04 at 21:50