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Recognition of Targets in Camera Networks
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
2008 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis presents a re-recognition model for use in area camera network surveillance systems. The method relies on a mix of covariance matrix fea- ture descriptions and Bayesian networks for topological information. The system consists of an object recognition model and an re-recognition model. The object recognition model is responsible for separating people from the background and generating the position and description for each person and frame. This is done by using a foreground-background segmen- tation model to separate the background from a person. The segmented image is then tracked by a tracking algorithm that produces the coordinates for each person. It is also responsible for creating a silhouette that is used to create a feature vector consisting of a covariance matrix that describes the persons appearance. A hypothesis engine is then responsible for connecting the coordinates into a continues track that describes the trajectory were aa person has been visiting.

Every trajectory is stored and available to the re-recognition model. It then compares two covariance matrices using a sophisticated distance me- thod to generate a probabilistic score value. The score is then combined with a likelihood-value of the topological match generated with a Bayesian network structure containing gathered statistical data. The topological in- formation is mainly intended to ¯lter the most un-likely matches.

Place, publisher, year, edition, pages
2008. , 45 p.
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-95351ISRN: LiU-ITN-TEK-A--08/120--SEOAI: diva2:635987
Subject / course
Media Technology
Available from: 2013-07-08 Created: 2013-07-03 Last updated: 2013-07-08Bibliographically approved

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Johansson, Fredrik
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