Understanding and Using Context
Personal and Ubiquitous Computing
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Context in problem solving: a survey
The Knowledge Engineering Review
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contextual recognition of head gestures
ICMI '05 Proceedings of the 7th international conference on Multimodal interfaces
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)
Context-Based Reasoning Using Ontologies to Adapt Visual Tracking in Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Determining the best suited semantic events for cognitive surveillance
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Expert Systems with Applications: An International Journal
Opportunistic sensor interpretation in a virtual smart environment
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Hi-index | 12.06 |
Tracking algorithms in computer vision usually fail when dealing with complex scenarios. This paper presents an extension of a general tracking system that uses context knowledge to solve tracking issues. The context layer represents knowledge about the context of the analyzed scenario and applies rules to reason with it, in order to assess the general tracking layer at different stages and enhance tracking results. The context knowledge representation and the reasoning methods are general and can be easily adapted to different scenarios. The experimentation results show that the performance of the tracking system is considerably improved, while the efficiency requirements that are mandatory in real-time systems are satisfied.