Cascaded Confidence Filtering for Improved Tracking-by-Detection
Abstract
We propose a novel approach to increase the robustness of object detection algorithms in surveillance scenarios. The cascaded confidence filter successively incorporates constraints on the size of the objects, on the preponderance of the background and on the smoothness of trajectories. In fact, the continuous detection confidence scores are analyzed locally to adapt the generic detector to the specific scene. The approach does not learn specific object models, reason about complete trajectories or scene structure, nor use multiple cameras. Therefore, it can serve as preprocessing step to robustify many tracking-by-detection algorithms. Our real-world experiments show significant improvements, especially in the case of partial occlusions, changing backgrounds, and similar distractors.
Cite
Text
Stalder et al. "Cascaded Confidence Filtering for Improved Tracking-by-Detection." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15549-9_27Markdown
[Stalder et al. "Cascaded Confidence Filtering for Improved Tracking-by-Detection." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/stalder2010eccv-cascaded/) doi:10.1007/978-3-642-15549-9_27BibTeX
@inproceedings{stalder2010eccv-cascaded,
title = {{Cascaded Confidence Filtering for Improved Tracking-by-Detection}},
author = {Stalder, Severin and Grabner, Helmut and Van Gool, Luc},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {369-382},
doi = {10.1007/978-3-642-15549-9_27},
url = {https://mlanthology.org/eccv/2010/stalder2010eccv-cascaded/}
}