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Mathematical Problems in Engineering
Volume 2014, Article ID 654790, 14 pages
Research Article

Sensor Selection and Integration to Improve Video Segmentation in Complex Environments

1Old Dominion University, Norfolk, VA 23529, USA
2National Central University, Jhongli 32001, Taiwan

Received 1 November 2013; Accepted 28 December 2013; Published 9 February 2014

Academic Editor: Yi-Hung Liu

Copyright © 2014 Adam R. Reckley et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background subtraction is often considered to be a required stage of any video surveillance system being used to detect objects in a single frame and/or track objects across multiple frames in a video sequence. Most current state-of-the-art techniques for object detection and tracking utilize some form of background subtraction that involves developing a model of the background at a pixel, region, or frame level and designating any elements that deviate from the background model as foreground. However, most existing approaches are capable of segmenting a number of distinct components but unable to distinguish between the desired object of interest and complex, dynamic background such as moving water and high reflections. In this paper, we propose a technique to integrate spatiotemporal signatures of an object of interest from different sensing modalities into a video segmentation method in order to improve object detection and tracking in dynamic, complex scenes. Our proposed algorithm utilizes the dynamic interaction information between the object of interest and background to differentiate between mistakenly segmented components and the desired component. Experimental results on two complex data sets demonstrate that our proposed technique significantly improves the accuracy and utility of state-of-the-art video segmentation technique.