Globally Optimal Solution to Multi-Object Tracking with Merged Measurements
Abstract
Multiple object tracking has been formulated recently as a global optimization problem, and solved efficiently with optimal methods such as the Hungarian Algorithm. A severe limitation is the inability to model multiple objects that are merged into a single measurement, and track them as a group, while retaining optimality. This work presents a new graph structure that encodes these multiple-match events as standard one-to-one matches, allowing computation of the solution in polynomial time. Since identities are lost when objects merge, an efficient method to identify groups is also presented, as a flow circulation problem. The problem of tracking individual objects across groups is then posed as a standard optimal assignment. Experiments show increased performance on the PETS 2006 and 2009 datasets compared to state-of-the-art algorithms.
Cite
Text
Henriques et al. "Globally Optimal Solution to Multi-Object Tracking with Merged Measurements." IEEE/CVF International Conference on Computer Vision, 2011. doi:10.1109/ICCV.2011.6126532Markdown
[Henriques et al. "Globally Optimal Solution to Multi-Object Tracking with Merged Measurements." IEEE/CVF International Conference on Computer Vision, 2011.](https://mlanthology.org/iccv/2011/henriques2011iccv-globally/) doi:10.1109/ICCV.2011.6126532BibTeX
@inproceedings{henriques2011iccv-globally,
title = {{Globally Optimal Solution to Multi-Object Tracking with Merged Measurements}},
author = {Henriques, João F. and Caseiro, Rui and Batista, Jorge P.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2011},
pages = {2470-2477},
doi = {10.1109/ICCV.2011.6126532},
url = {https://mlanthology.org/iccv/2011/henriques2011iccv-globally/}
}