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Proceedings Paper

Object tracking using multiple camera video streams
Author(s): Mehrube Mehrubeoglu; Diego Rojas; Lifford McLauchlan
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Paper Abstract

Two synchronized cameras are utilized to obtain independent video streams to detect moving objects from two different viewing angles. The video frames are directly correlated in time. Moving objects in image frames from the two cameras are identified and tagged for tracking. One advantage of such a system involves overcoming effects of occlusions that could result in an object in partial or full view in one camera, when the same object is fully visible in another camera. Object registration is achieved by determining the location of common features in the moving object across simultaneous frames. Perspective differences are adjusted. Combining information from images from multiple cameras increases robustness of the tracking process. Motion tracking is achieved by determining anomalies caused by the objects' movement across frames in time in each and the combined video information. The path of each object is determined heuristically. Accuracy of detection is dependent on the speed of the object as well as variations in direction of motion. Fast cameras increase accuracy but limit the speed and complexity of the algorithm. Such an imaging system has applications in traffic analysis, surveillance and security, as well as object modeling from multi-view images. The system can easily be expanded by increasing the number of cameras such that there is an overlap between the scenes from at least two cameras in proximity. An object can then be tracked long distances or across multiple cameras continuously, applicable, for example, in wireless sensor networks for surveillance or navigation.

Paper Details

Date Published: 4 May 2010
PDF: 10 pages
Proc. SPIE 7724, Real-Time Image and Video Processing 2010, 77240L (4 May 2010); doi: 10.1117/12.854091
Show Author Affiliations
Mehrube Mehrubeoglu, Texas A&M Univ. Corpus Christi (United States)
Diego Rojas, Texas A&M Univ. Corpus Christi (United States)
Lifford McLauchlan, Texas A&M Univ.-Kingsville (United States)

Published in SPIE Proceedings Vol. 7724:
Real-Time Image and Video Processing 2010
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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