高レベルアブダクションによる視覚的説明：移動物体に関する回答集合プログラミング駆動推論
Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects
時空間トラックレットを対象に、仮説形成・信念修正・デフォルト推論を統合した回答集合プログラミングによるアブダクションと物体追跡パイプラインを組み合わせ、動画データの頑健な視覚的説明を計算するハイブリッド手法を提案した。
著者: Jakob Suchan, Mehul Bhatt, Przemysław Wałęga, Carl Schultz
分類: cs.AI, cs.CV, cs.LO, cs.RO
原文アブストラクト
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.
