A Framework for Human Tracking Using Kalman Filter and Fast Mean Shift Algorithms
Abstract
The task of reliable detection and tracking of multiple objects becomes highly complex for crowded scenarios. In this paper, a robust framework is presented for multi-Human tracking. It includes a combination of Kalman filter and fast mean shift algorithm. Kalman prediction is measurement follower. It may be misled by wrong measurement. The search for solution is guided by a fast mean shift procedure. It is used to locate densities extrema, which gives clue that whether Kalman prediction is right or it is misled by wrong measurement. Tracking results are demonstrated for crowded scenes and evaluation of the proposed tracking framework is presented.
Cite
Text
Ali and Terada. "A Framework for Human Tracking Using Kalman Filter and Fast Mean Shift Algorithms." IEEE/CVF International Conference on Computer Vision Workshops, 2009. doi:10.1109/ICCVW.2009.5457591Markdown
[Ali and Terada. "A Framework for Human Tracking Using Kalman Filter and Fast Mean Shift Algorithms." IEEE/CVF International Conference on Computer Vision Workshops, 2009.](https://mlanthology.org/iccvw/2009/ali2009iccvw-framework/) doi:10.1109/ICCVW.2009.5457591BibTeX
@inproceedings{ali2009iccvw-framework,
title = {{A Framework for Human Tracking Using Kalman Filter and Fast Mean Shift Algorithms}},
author = {Ali, Ahmed and Terada, Kenji},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2009},
pages = {1028-1033},
doi = {10.1109/ICCVW.2009.5457591},
url = {https://mlanthology.org/iccvw/2009/ali2009iccvw-framework/}
}