CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Communications of the ACM - A game experience in every application
Out of context: computer systems that adapt to, and learn from, context
IBM Systems Journal
Experiential Sampling for video surveillance
IWVS '03 First ACM SIGMM international workshop on Video surveillance
Experiential sampling for monitoring
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
Probability fusion for correlated multimedia streams
Proceedings of the 12th annual ACM international conference on Multimedia
Detection of a speaker in video by combined analysis of speech sound and mouth movement
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
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We present a novel experience based sampling or experiential sampling technique which has the ability to focus on the analysis's task by making use of the contextual information from the environment. In this technique, sensor samples are used to gather information about the current environment and attention samples are used to represent the current state of attention. The task-attended samples are inferred from experience and maintained by a sampling based dynamical system. The multimedia analysis task can then focus on the attention samples only. Moreover, past experiences and the current environment can be used to adaptively correct and tune the attention. Experimental results have been presented to demonstrate the efficacy of our technique.