Multi-Kernel Object Tracking
- 24 October 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 19457871,p. 1234-1237
- https://doi.org/10.1109/icme.2005.1521651
Abstract
In this paper, we present an object tracking algorithm for the low-frame-rate video in which objects have fast motion. The conventional mean-shift tracking fails in case the relocation of an object is large and its regions between the consecutive frames do not overlap. We provide a solution to this problem by using multiple kernels centered at the high motion areas. In addition, we improve the convergence properties of the mean-shift by integrating two likelihood terms, background and template similarities, in the iterative update mechanism. Our simulations prove the effectiveness of the proposed methodKeywords
This publication has 4 references indexed in Scilit:
- Adaptive background mixture models for real-time trackingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Real-time tracking of non-rigid objects using mean shiftPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Wallflower: principles and practice of background maintenancePublished by Institute of Electrical and Electronics Engineers (IEEE) ,1999
- Pfinder: real-time tracking of the human bodyPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997