Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 1326-1335.
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 hypotheses, multiple measurements are acquired at each time instant. The model switches among a set of hypothesized measurements during the propagation. Two computationally efficient filtering algorithms are derived for online joint tracking. Both the occlusion relationship and state of the objects are recursively estimated from the history of measurement data. The switching hypothesized measurements (SHM) model is generally applicable to describe various dynamic processes with multiple alternative measurement methods.
@inproceedings{ijcai2003p188,
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 = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {1326--1335},
year = {2003},
url = {https://ijcai.org/proceedings/2003/188},
}
TY - CPAPER TI - Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking AU - Wang, Yang AU - Tan, Tele AU - Loe, Kia-Fock T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 1326 EP - 1335 UR - https://ijcai.org/proceedings/2003/188 ER -Copied
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