Robust Object Tracking with a Case-Base Updating Strategy
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 925-929.
Abstract
The paper describes a simple but effective framework for visual object tracking in video sequences. The main contribution of this work lies in the introduction of a case-based reasoning (CBR) method to maintain an accurate target model automatically and efficiently under significant appearance changes without drifting away. Specifically, an automatic case-base maintenance algorithm is proposed to dynamically update the case base, manage the case base to be competent and representative, and to maintain the case base in a reasonable size for real-time performance. Furthermore, the method can provide an accurate confidence measurement for each tracked object so that the tracking failures can be identified in time. Under the framework, a real-time face tracker is built to track human faces robustly under various face orientations, significant facial expressions, and illumination changes.
@inproceedings{ijcai2007p149,
title = {Robust Object Tracking with a Case-Base Updating Strategy},
author = {Liao, Wenhui and Tong, Yan and Zhu, Zhiwei and Ji, Qiang},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {925--929},
year = {2007},
url = {https://ijcai.org/proceedings/2007/149},
}
TY - CPAPER TI - Robust Object Tracking with a Case-Base Updating Strategy AU - Liao, Wenhui AU - Tong, Yan AU - Zhu, Zhiwei AU - Ji, Qiang T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 925 EP - 929 UR - https://ijcai.org/proceedings/2007/149 ER -Copied
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