DocumentCode :
3549075
Title :
Real-time tracking using level sets
Author :
Shi, Yonggang ; Karl, W. Clem
Author_Institution :
Electr. & Comput. Eng. Dept., Boston Univ., MA, USA
Volume :
2
fYear :
2005
fDate :
20-25 June 2005
Firstpage :
34
Abstract :
In this paper we propose a novel implementation of the level set method that achieves real-time level-set-based video tracking. In our fast algorithm, the evolution of the curve is realized by simple operations such as switching elements between two linked lists and there is no need to solve any partial differential equations. Furthermore, a novel procedure based on Gaussian filtering is introduced to incorporate boundary smoothness regularization. By replacing the standard curve length penalty with this new smoothing procedure, further speedups are obtained. Another advantage of our fast algorithm is that the topology of the curves can be controlled easily. For the tracking of multiple objects, we extend our fast algorithm to maintain the desired topology for multiple object boundaries based on ideas from discrete topology. With our fast algorithm, a real-time system has been implemented on a standard PC and only a small fraction of the CPU power is used for tracking. Results from standard test sequences and our realtime system are presented.
Keywords :
Gaussian processes; computer vision; curve fitting; filtering theory; object detection; partial differential equations; real-time systems; tracking; video signal processing; Gaussian filtering process; boundary smoothness regularization; curve evolution; curve topology; level set method; linked lists; multiple object tracking; partial differential equation; real-time system; real-time tracking; standard PC; video tracking; Filtering; Level set; Narrowband; Partial differential equations; Real time systems; Smoothing methods; System testing; Topology; Video surveillance; Videoconference;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2372-2
Type :
conf
DOI :
10.1109/CVPR.2005.294
Filename :
1467420
Link To Document :
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