Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning About Moving Objects

We propose a hybrid architecture for systematically computing robust visual explanation(s) encompassing hypothesis formation, belief revision, and default reasoning with video data. The architecture consists of two tightly integrated synergistic components: (1) (functional) answer set programming based abductive reasoning with SPACE-TIME TRACKLETS as native entities; and (2) a visual processing pipeline for detection based object tracking and motion analysis. We present the formal framework, its general implementation as a (declarative) method in answer set programming, and an example application and evaluation based on two diverse video datasets: the MOTChallenge benchmark developed by the vision community, and a recently developed Movie Dataset.

Authors

• Jakob Suchan
• Mehul Bhatt
• Przemysław Wałega
• Carl Schultz

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