Quast K, Kaup A (2011)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2011
Publisher: Hindawi Publishing Corporation / SpringerOpen / Springer Verlag (Germany)
Book Volume: 2011
Pages Range: 14
Article Number: 814285
DOI: 10.1155/2011/814285
A complete video surveillance system for automatically tracking shape and position of objects in traffic scenarios is presented. The system, called Auto GMM-SAMT, consists of a detection and a tracking unit. The detection unit is composed of a Gaussian mixture model- (GMM-) based moving foreground detection method followed by a method for determining reliable objects among the detected foreground regions using a projective transformation. Unlike the standard GMM detection the proposed detection method considers spatial and temporal dependencies as well as a limitation of the standard deviation leading to a faster update of the mixture model and to smoother binary masks. The binary masks are transformed in such a way that the object size can be used for a simple but fast classification. The core of the tracking unit, named GMM-SAMT, is a shape adaptive mean shift- (SAMT-) based tracking technique, which uses Gaussian mixture models to adapt the kernel to the object shape. GMM-SAMT returns not only the precise object position but also the current shape of the object. Thus, Auto GMM-SAMT achieves good tracking results even if the object is performing out-of-plane rotations. Copyright © 2011 Katharina Quast and Andr Kaup.
APA:
Quast, K., & Kaup, A. (2011). AUTO GMM-SAMT: An automatic object tracking system for video surveillance in traffic scenarios. Eurasip Journal on Image and Video Processing, 2011, 14. https://dx.doi.org/10.1155/2011/814285
MLA:
Quast, Katharina, and André Kaup. "AUTO GMM-SAMT: An automatic object tracking system for video surveillance in traffic scenarios." Eurasip Journal on Image and Video Processing 2011 (2011): 14.
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