Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking
Abstract
This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tracking multiple objects jointly.For a set of occlusion relationship hypotheses, a frame is measured once under each hypothesis, and a set of measurements is obtained at each time instant.Both the occlusion relationship and state of the objects are recursively estimated from the history of measurement data.Two computationally efficient filtering algorithms are derived for online joint tracking.
Cite
Text
Wang et al. "Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking." International Joint Conference on Artificial Intelligence, 2003.Markdown
[Wang et al. "Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking." International Joint Conference on Artificial Intelligence, 2003.](https://mlanthology.org/ijcai/2003/wang2003ijcai-switching/)BibTeX
@inproceedings{wang2003ijcai-switching,
title = {{Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking}},
author = {Wang, Yang and Tan, Tele and Loe, Kia-Fock},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2003},
pages = {1326-1336},
url = {https://mlanthology.org/ijcai/2003/wang2003ijcai-switching/}
}