Abstract
We demonstrate the need and potential of systematically integrated vision and semantics solutions for visual sensemaking (in the backdrop of autonomous driving).
A general method for online visual sensemaking using answer set programming is systematically formalised and fully implemented. The method integrates state of the art in visual computing, and is developed as a modular framework usable within hybrid architectures for perception & control. We evaluate and demo with community established benchmarks KITTIMOD and MOT. As use-case, we focus on the significance of human-centred visual sensemaking ---e.g., semantic representation and explainability, question-answering, commonsense interpolation--- in safety-critical autonomous driving situations.
Keywords
- Knowledge Representation and Reasoning: Non-monotonic Reasoning
- Computer Vision: Structural and Model-Based Approaches, Knowledge Representation and Reasoning
- Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
- Computer Vision: Motion and Tracking
