Quast K, Kaup A (2012)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2012
Publisher: Springer
Edited Volumes: Analysis, Retrieval and Delivery of Multimedia Content
Series: Lecture Notes in Electrical Engineering
City/Town: New York, NY
Book Volume: 158
Pages Range: 107-122
ISBN: 978-1-4614-3831-1
DOI: 10.1007/978-1-4614-3831-1_7
GMM-SAMT, a new object tracking algorithm based on a combination of the mean shift principal and Gaussian mixture models is presented. GMM-SAMT uses an asymmetric shape adapted kernel, instead of a symmetrical one like in traditional mean shift tracking. During the mean shift iterations the kernel scale is altered according to the object scale, providing an initial adaption of the object shape. The final shape of the kernel is then obtained by segmenting the area inside and around the adapted kernel into object and non-object segments using Gaussian mixture models.
APA:
Quast, K., & Kaup, A. (2012). Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models. In N. Adami, A. Cavallaro, R. Leonardi, P. Migliorati (Eds.), Analysis, Retrieval and Delivery of Multimedia Content. (pp. 107-122). New York, NY: Springer.
MLA:
Quast, Katharina, and André Kaup. "Shape Adaptive Mean Shift Object Tracking Using Gaussian Mixture Models." Analysis, Retrieval and Delivery of Multimedia Content. Ed. N. Adami, A. Cavallaro, R. Leonardi, P. Migliorati, New York, NY: Springer, 2012. 107-122.
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