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Visual Explanation by High-Level Abduction: On Answer-Set Programming Driven Reasoning about Moving Objects
HCC Lab., University of Bremen, Bremen, Germany.
Örebro University, School of Science and Technology. HCC Lab., University of Bremen, Bremen, Germany; MPI Lab., Örebro University, Örebro, Sweden; . (AASS)ORCID iD: 0000-0002-6290-5492
University of Warsaw, Warsaw, Poland.
Aarhus University, Aarhus, Denmark.
2018 (English)In: AAAI 2018: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018Conference paper, Published paper (Refereed)
Abstract [en]

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

Place, publisher, year, edition, pages
2018.
Keyword [en]
artificial intelligence, cognitive vision, knowledge representation and reasoning, computer vision, robotics
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-63758OAI: oai:DiVA.org:oru-63758DiVA: diva2:1170286
Conference
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, USA, February 2-7, 2018
Available from: 2018-01-02 Created: 2018-01-02 Last updated: 2018-01-13Bibliographically approved

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Bhatt, Mehul

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