Author:
Joanna Isabelle Olszewska
Affiliation:
University of Gloucestershire, United Kingdom
Keyword(s):
Surveillance Application, Visual Scene Analysis, Automated Scene Understanding, Knowledge Representation, Spatio-temporal Visual Ontology, Symbolic Reasoning, Computer Vision, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Knowledge Representation and Reasoning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
;
Symbolic Systems
Abstract:
Reliable detection of objects of interest in complex visual scenes is of prime importance for video-surveillance applications. While most vision approaches deal with tracking visible or partially visible objects in single or multiple video streams, we propose a new approach to automatically detect all objects of interest being part of an analyzed scene, even those entirely hidden in a camera view whereas being present in the scene. For that, we have developed an innovative artificial-intelligence framework embedding a computer vision process fully integrating symbolic knowledge-based reasoning. Our system has been evaluated on standard datasets consisting of video streams with real-world objects evolving in cluttered, outdoor environment under difficult lighting conditions. Our proposed approach shows excellent performance both in detection accuracy and robustness, and outperforms state-of-the-art methods.