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
Parallel hypothesis driven video content analysis
Proceedings of the 2004 ACM symposium on Applied computing
Event detection for video surveillance using an expert system
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Detecting motion patterns via direction maps with application to surveillance
Computer Vision and Image Understanding
Recurrent Bayesian network for the recognition of human behaviors from video
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Context based object detection from video
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Action recognition with semi-global characteristics and hidden Markov models
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
MetroSurv: detecting events in subway stations
Multimedia Tools and Applications
Intent inference via syntactic tracking
Digital Signal Processing
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The goal of this paper is to describe and demonstrate the application of Bayesian networks in a generic automatic video surveillance system. Taking image features of tracked moving regions from an image sequence as input, mobile object properties are first computed and noise is suppressed by statistical methods. The probability that a scenario occurs is then computed from these mobile object properties through several layers of naive Bayesian classifiers (or a Bayesian network). Several issues and solutions regarding the efficiency of the Bayesian network are discussed. For example, the parameters of the networks, which represent rare activities (typical of video surveillance applications), can be learned from image sequences of similar scenarios, which are more common. We demonstrate the effectiveness of our approach by training the networks with 600 image frames belonging to one domain of interest and applying them to image sequences in a different domain.