Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Scalable Independent Multi-level Distribution in Multimedia Content Analysis
IDMS/PROMS 2002 Proceedings of the Joint International Workshops on Interactive Distributed Multimedia Systems and Protocols for Multimedia Systems: Protocols and Systems for Interactive Distributed Multimedia
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Extracting semantics from audio-visual content: the final frontier in multimedia retrieval
IEEE Transactions on Neural Networks
Supporting timeliness and accuracy in distributed real-time content-based video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Parallel hypothesis driven video content analysis
Proceedings of the 2004 ACM symposium on Applied computing
Real-time video content analysis: QoS-aware application composition and parallel processing
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
An improved classification for parallel inference framework with hierarchy
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
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Dynamic Bayesian networks are a promising approach to automatic video content analysis which allows statistical inference and learning to be combined with domain knowledge. When the particle filter (PF) is used for approximate inference, video data can often be classified in real-time (supporting e.g. on-line automatic video surveillance). Unfortunately, the limited processing resources available on a typical host restricts the complexity, accuracy, and frame rate of PF classification tasks. Here, we target this limitation by applying the traditional parallel pooled classifiers architecture to execute multiple PFs in parallel and to coordinate their output.We then identify a significant weakness of this approach in terms of loss of accuracy. To reduce the loss of accuracy, we propose a novel scheme for coordinating the pooled PFs based on the exchange of so-called particles. In an object tracking experiment, a significant loss of accuracy is observed for the naive application of the pooled classifiers architecture. No loss of accuracy is detected when our scheme for exchanging particles is used.