From image sequences towards conceptual descriptions
Image and Vision Computing
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
HTN planning: complexity and expressivity
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
A comparative analysis of partial order planning and task reduction planning
ACM SIGART Bulletin
Natural Language Description of Image Sequences as a Form of Knowledge Representation
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Visualisation of Conceptual Descriptions Derived from Image Sequences
Mustererkennung 1999, 21. DAGM-Symposium
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
SHOP: simple hierarchical ordered planner
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Total-order planning with partially ordered subtasks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Cognitive vision: The case for embodied perception
Image and Vision Computing
Natural Language Descriptions of Human Behavior from Video Sequences
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
| Hi-index | 0.00 |
The algorithmic generation of textual descriptions of image sequences requires conceptual knowledge. In our case, a stationary camera recorded image sequences of road traffic scenes. The necessary conceptual knowledge has been provided in the form of a so-called Situation Graph Tree (SGT). Other endeavors such as the generation of a synthetic image sequence from a textual description or the transformation of machine vision results for use in a driver assistance system could profit from the exploitation of the same conceptual knowledge, but more in a planning (pre-scriptive) rather than a de-scriptive context.A recently discussed planning formalism, Hierarchical Task Networks (HTNs), exhibits a number of formal similarities with SGTs. These suggest to investigate whether and to which extent SGTs may be re-cast as HTNs in order to re-use the conceptual knowledge about the behavior of vehicles in road traffic scenes for planning purposes.