Image Sequence Evaluation: 30 Years and Still Going Strong
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Steps toward a cognitive vision system
AI Magazine
Representation of occurrences for road vehicle traffic
Artificial Intelligence
Understanding dynamic scenes based on human sequence evaluation
Image and Vision Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Towards high-level human activity recognition through computer vision and temporal logic
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Human activity analysis: A review
ACM Computing Surveys (CSUR)
A large-scale benchmark dataset for event recognition in surveillance video
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
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Fuzzy metric temporal logic (FMTL) and situation graph trees (SGTs) have been shown to be promising tools in high-level situation recognition. They generate semantic descriptions from numeric perceptual data. FMTL and SGTs allow for sophisticated and universally applicable rule-based expert systems. Dealing with incomplete data is still a challenging task for rule-based systems. The FMTL/SGT system is extended by interpolation and hallucination to become capable of incomplete data. Therefore, one analysis to the robustness of the FMTL/SGT system in situation recognition is removing parts of the ground truth input tracks. The recognition results are compared to ground truth for situations such as "load object into car". The results show that the presented approach is robust against incomplete data. The contribution of this work is, first, an extension to the FMTL/SGT system to handle incomplete data via interpolation and hallucination, second, a knowledge base for recognizing vehicle-centered situations.