Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking
Abstract
In this paper, we propose a particle filtering approach for tracking applications in image sequences. The system we propose combines a measurement equation and a dynamic equation which both depend on the image sequence. Taking into account several possible observations, the likelihood is modeled as a linear combination of Gaussian laws. Such a model allows inferring an analytic expression of the optimal importance function used in the diffusion process of the particle filter. It also enables building a relevant approximation of a validation gate. We demonstrate the significance of this model for a point tracking application.
Cite
Text
Arnaud and Mémin. "Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-24672-5_24Markdown
[Arnaud and Mémin. "Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/arnaud2004eccv-optimal/) doi:10.1007/978-3-540-24672-5_24BibTeX
@inproceedings{arnaud2004eccv-optimal,
title = {{Optimal Importance Sampling for Tracking in Image Sequences: Application to Point Tracking}},
author = {Arnaud, Elise and Mémin, Étienne},
booktitle = {European Conference on Computer Vision},
year = {2004},
pages = {302-314},
doi = {10.1007/978-3-540-24672-5_24},
url = {https://mlanthology.org/eccv/2004/arnaud2004eccv-optimal/}
}