Rule-Based Multiple Object Tracking for Traffic Surveillance Using Collaborative Background Extraction

In order to address the challenges of occlusions and background variations, we propose a novel and effective rule-based multiple object tracking system for traffic surveillance using a collaborative background extraction algorithm. The collaborative background extraction algorithm collaboratively ex...

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Bibliographic Details
Published inAdvances in Visual Computing Vol. 4842; pp. 469 - 478
Main Authors Su, Xiaoyuan, Khoshgoftaar, Taghi M., Zhu, Xingquan, Folleco, Andres
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2007
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
Image processing
Pattern recognition
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Summary:In order to address the challenges of occlusions and background variations, we propose a novel and effective rule-based multiple object tracking system for traffic surveillance using a collaborative background extraction algorithm. The collaborative background extraction algorithm collaboratively extracts a background from multiple independent extractions to remove spurious background pixels. The rule-based strategies are applied for thresholding, outlier removal, object consolidation, separating neighboring objects, and shadow removal. Empirical results show that our multiple object tracking system is highly accurate for traffic surveillance under occlusion conditions.
ISBN:9783540768555
3540768556
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-540-76856-2_46