  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.
