ML Anthology

Multi-Objective Detector and Tracker Parameter Optimization via NSGA-II

Ryan Fogle, Karl Salva, Juan Vasquez, Ash Kessler
WACVW 2015 pp. 4-9
doi:10.1109/WACVW.2015.13 /wacvw/2015/fogle2015wacvw-multiobjective/

Abstract

Modern tracking algorithms must engage a wide variety of targets. These targets vary in size, shape, intensity, and speed. While the targets change dependent upon application, oftentimes the tracking software remains predominantly constant. Rather, the tracking algorithm flexibility is achieved by user-defined parameters. Unfortunately even for experienced operators, these parameters may be difficult to tune resulting in suboptimal performance. This difficulty prompts the need for automated tuning software. To aid the operator in determining parameter values, this paper presents the novel application of non-dominated sort genetic algorithm II (NSGA-II) to determine optimal detector and tracker settings.

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Cite

Text

Fogle et al. "Multi-Objective Detector and Tracker Parameter Optimization via NSGA-II." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2015. doi:10.1109/WACVW.2015.13

Markdown

[Fogle et al. "Multi-Objective Detector and Tracker Parameter Optimization via NSGA-II." IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, 2015.](https://mlanthology.org/wacvw/2015/fogle2015wacvw-multiobjective/) doi:10.1109/WACVW.2015.13

BibTeX

@inproceedings{fogle2015wacvw-multiobjective,
  title     = {{Multi-Objective Detector and Tracker Parameter Optimization via NSGA-II}},
  author    = {Fogle, Ryan and Salva, Karl and Vasquez, Juan and Kessler, Ash},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision Workshops},
  year      = {2015},
  pages     = {4-9},
  doi       = {10.1109/WACVW.2015.13},
  url       = {https://mlanthology.org/wacvw/2015/fogle2015wacvw-multiobjective/}
}