Efficient block-division model for robust multiple object tracking
Date
2011
Authors
Luo, Wenhan
Zhang, Xiaoqin
Liu, Yang
Li, Xi
Hu, Weiming
Li, Wei
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
2011 IEEE International Conference on Acoustics, Speech, and Signal Processing : Proceedings, 2011: pp.1205-1208
Statement of Responsibility
Wenhan Luo, Xiaoqin Zhang, Yang Liu, Xi Li, Weiming Hu, Wei Li
Conference Name
IEEE International Conference on Acoustics, Speech and Signal Processing (36th : 2011 : Prague, Czech Republic)
ICASSP 2011
ICASSP 2011
Abstract
Tracking multiple objects under occlusion is one of the most challenging issues in computer vision. Occlusion results in mistaken match when finding the most similar candidate. Adapting to the change of objects is essential for tracking as objects often undergo intrinsic changes, but noise is unavoidably introduced during updating of the object, and this further confuses the tracker. In order to address these problems, a block-division appearance model is introduced to efficiently handle occlusion. In this model, spatial information is introduced to avoid the mistaken match between object and candidate. Based on this model, a selective updating strategy is proposed to incrementally learn the change of the object, avoiding introducing noise when updating. At the same time occlusion is deduced by monitoring the variation of each block. Experimental results in various videos validate the effectiveness of our algorithm in tracking multiple objects under occlusion.
School/Discipline
School of Computer Science
Dissertation Note
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Rights
Copyright ©2011 by the Institute of Electrical and Electronics Engineers, Inc. All rights reserved.