MUSCLE - WP11: Grand Challenges
Local Webpage Bilkent University, Turkey
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MUSCLE - WP11: Grand Challenges |
 WP11 Scientific Meetings |
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Roman Goldenberg, Ron Kimmel, Ehud Rivlin and Michael Rudzsky Computer Science Department, Technion-Israel Institute of Technolog, Israel.
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Title: Behavior classification by eigendecomposition of periodic motions Abstract: We show how periodic motions can be represented by a small number of eigenshapes that capture the whole dynamic mechanism of the motion. Spectral decomposition of a silhouette of a moving object serves as a basis for behavior classification by principle component analysis. The boundary contour of the walking dog, for example, is first computed efficiently and accurately. After normalization, the implicit representation of a sequence of silhouette contours given by their corresponding binary images, is used for generating eigenshapes for the given motion. Singular value decomposition produces these eigenshapes that are then used to analyze the sequence. We show examples of object as well as behavior classification based on the eigendecomposition of the binary silhouette sequence. |
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Ismail Sengor Altingovde, Ugur Gudukbay and Ozgur Ulusoy Computer Engineering Department, Bilkent University,Turkey |
Title: BilVideo Video Database System Abstract: Soon. |
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Herbert Ramoser and Csaba Beleznai Advanced Computer Vision GmbH - ACV, Austria. |
Title: Tracking multiple humans using fast mean-shift mode seeking Abstract: Tracking multiple targets - such as humans in a busy scene - is a non-trivial task due to the frequent occlusions occurring between the target objects. This work describes a novel way to detect human candidates directly from the non-thresholded difference image obtained by background differencing and to track detected candidates by a fast variant of the mean shift procedure. For occluding targets, a model-based search step is performed to search for the local configuration best explaining the observed distribution in the difference image. The proposed technique shows real-time performance for challenging multi-target scenarios. Presented results are compared to a conventional blob-based tracker and evaluated in terms of detection performance. |
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Ugur Toreyin, Yigithan Dedeoglu, Caglar Ari and A. Enis Cetin |
Title: Falling person detection using HMM
modeling of audio and video Abstract: Soon. |
| Title: Calibrating cameras in cluttered
environment and registering moving people Abstract: Soon. |
