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Proceedings Paper

Layer-based object detection and tracking with graph matching
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Paper Abstract

Automatic object detection and tracking has been widely applied in the video surveillance systems for homeland security and data fusion in the remote sensing and airborne imagery. The typical applications include human motion analysis and the vehicle detection. Here we implement object detection and tracking under shape graphs of interesting objects integrating local contextual information (corner/point features, etc) of the objects. On the top layer, shapes/sketches provide a discrimination measure to describe the global status of the interesting objects. This kind of information is very useful to improve the object tracking performance for occlusion. The shape can be modeled as a graph or hyper graph through its local geometric features. On the bottom layer, local geometric features are used to capture local properties of objects and perform correspondence estimation of high-level shapes. The local features provide a way to conquer inaccurate object segmentation and extraction. The experiments were implemented on human face tracking and vehicle detection and tracking.

Paper Details

Date Published: 25 May 2011
PDF: 10 pages
Proc. SPIE 8020, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII, 80200N (25 May 2011); doi: 10.1117/12.883508
Show Author Affiliations
Qiang He, Mississippi Valley State Univ. (United States)
Chee-Hung Henry Chu, Univ. of Louisiana at Lafayette (United States)

Published in SPIE Proceedings Vol. 8020:
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII
Daniel J. Henry; Beato T. Cheng; Dale C. Linne von Berg; Darrell L. Young, Editor(s)

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