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A multicast scheme for parallel software-only video effects processing
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IFIP/ACM International Conference on Distributed systems platforms
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Bayesian Networks and Decision Graphs
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Machine Learning
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IEEE Intelligent Systems
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On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
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SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
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IEEE Transactions on Multimedia
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IEEE Journal on Selected Areas in Communications
A case for end system multicast
IEEE Journal on Selected Areas in Communications
Prediction and tracking of moving objects in image sequences
IEEE Transactions on Image Processing
A generic approach to simultaneous tracking and verification in video
IEEE Transactions on Image Processing
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
On the Design of a SIP-Based Binding Middleware for Next Generation Home Network Services
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
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Real-Time content-based access to live video data requires content analysis applications that are able to process video streams in real-time and with an acceptable error rate. Statements such as this express quality of service (QoS) requirements. In general, control of the QoS provided can be achieved by sacrificing application quality in one QoS dimension for better quality in another, or by controlling the allocation of processing resources to the application. However, controlling QoS in video content analysis is particularly difficult, not only because main QoS dimensions like accuracy are nonadditive, but also because both the communication- and the processing-resource requirements are challenging.This article presents techniques for QoS-aware composition of applications for real-time video content analysis, based on dynamic Bayesian networks. The aim of QoS-aware composition is to determine application deployment configurations which satisfy a given set of QoS requirements. Our approach consists of: (1) an algorithm for QoS-aware selection of configurations of feature extractor and classification algorithms which balances requirements for timeliness and accuracy against available processing resources, (2) a distributed content-based publish/subscribe system which provides application scalability at multiple logical levels of distribution, and (3) scalable solutions for video streaming, filtering/transformation, feature extraction, and classification.We evaluate our approach based on experiments with an implementation of a real-time motion vector based object-tracking application. The evaluation shows that the application largely behaves as expected when resource availability and selections of configurations of feature extractor and classification algorithms vary. The evaluation also shows that increasing QoS requirements can be met by allocating additional CPUs for parallel processing, with only minor overhead.