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We propose a hybrid architecture for systematically computing robust visualexplanation(s) encompassing hypothesis formation, belief revision, and defaultreasoning with video data. The architecture consists of two tightly integratedsynergistic components: (1) (functional) answer set programming based abductivereasoning with space-time tracklets as native entities; and (2) a visualprocessing 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 andevaluation based on two diverse video datasets: the MOTChallenge benchmarkdeveloped by the vision community, and a recently developed Movie Dataset.

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