Contextual Priming for Object Detection
International Journal of Computer Vision
Model-Based Human Body Tracking
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Automatic Acquisition of Context Models and its Application to Video Surveillance
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
ACM Computing Surveys (CSUR)
Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR ... Papers (Lecture Notes in Computer Science)
On scene interpretation with description logics
Image and Vision Computing
Ontologies and the semantic web
Communications of the ACM - Surviving the data deluge
High-Level Data Fusion
A Context Model and Reasoning System to improve object trackingin complex scenarios
Expert Systems with Applications: An International Journal
Knowledge-assisted semantic video object detection
IEEE Transactions on Circuits and Systems for Video Technology
Modeling spatial-temporal context information in virtual worlds
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Improving the accuracy of action classification using view-dependent context information
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
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Classical tracking methods are often insufficient when dealing with complex scenarios. In order to solve tracking errors, innovative techniques based on the use of information about the context of the scene have been proposed. Context information ranges from precise measures computed on the pixels of the object neighborhood to high level representations of the entities and the activities of the scene. In this work, we focus on the second approach and propose an ontology-based extension of a general tracking procedure that reasons with abstract context descriptions to improve its accuracy. We describe the design of this extension and how reasoning is performed, as well as its advantages in surveillance scenarios.