ICIP 2006, Atlanta, GA

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Paper Detail

Session:Knowledge-Based Image Processing For Classification And Recognition In Surveillance Applications
Time:Wednesday, October 11, 10:00 - 10:20
Presentation: Special Session Lecture
Topic: Special Sessions: Integrating knowledge, semantics and image processing for classification and recognition...
Authors: Ciarán Ó Conaire; Dublin City University 
 Noel O'Connor; Dublin City University 
 Eddie Cooke; Dublin City University 
 Alan Smeaton; Dublin City University 
Abstract: This paper describes a system for object segmentation and feature extraction for surveillance video. Segmentation is performed by a dynamic vision system that fuses information from thermal infrared video with standard CCTV video in order to detect and track objects. Separate background modelling in each modality and dynamic mutual information based thresholding are used to provide initial foreground candidates for tracking. The belief in the validity of these candidates is ascertained using knowledge of foreground pixels and temporal linking of candidates. The Transferable Belief Model is used to combine these sources of information and segment objects. Extracted objects are subsequently tracked using adaptive thermo-visual appearance models. In order to facilitate search and classification of objects in large archives, retrieval features from both modalities are extracted for tracked objects. Overall system performance is demonstrated in a simple retrieval scenario.