Semantic Relational Object Tracking

This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on rich continuous attribute values measured from perceptual sensor data. A novel anchoring match...

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Published in:IEEE transactions on cognitive and developmental systems Vol. 12; no. 1; pp. 84 - 97
Main Authors: Persson, Andreas, Zuidberg Dos Martires, Pedro, De Raedt, Luc, Loutfi, Amy
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Anchoring
Ground truth
Object Tracking
Perceptual Anchoring
Probabilistic Logic Programming
Probabilistic Reasoning
Reasoning
Relational Particle Filtering
Semantic World Modeling
Semantics
Tracking
ISSN:2379-8920, 2379-8939, 2379-8939
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Abstract This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on rich continuous attribute values measured from perceptual sensor data. A novel anchoring matching function learns to maintain object entities in space and time and is validated using a large set of trained humanly annotated ground truth data of real-world objects. For more complex scenarios, a high-level probabilistic object tracker has been integrated with the anchoring framework and handles the tracking of occluded objects via reasoning about the state of unobserved objects. We demonstrate the performance of our integrated approach through scenarios such as the shell game scenario, where we illustrate how anchored objects are retained by preserving relations through probabilistic reasoning.
AbstractList This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on rich continuous attribute values measured from perceptual sensor data. A novel anchoring matching function learns to maintain object entities in space and time and is validated using a large set of trained humanly annotated ground truth data of real-world objects. For more complex scenarios, a high-level probabilistic object tracker has been integrated with the anchoring framework and handles the tracking of occluded objects via reasoning about the state of unobserved objects. We demonstrate the performance of our integrated approach through scenarios such as the shell game scenario, where we illustrate how anchored objects are retained by preserving relations through probabilistic reasoning.
Author Zuidberg Dos Martires, Pedro
Persson, Andreas
Loutfi, Amy
De Raedt, Luc
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SubjectTerms Anchoring
Ground truth
Object Tracking
Perceptual Anchoring
Probabilistic Logic Programming
Probabilistic Reasoning
Reasoning
Relational Particle Filtering
Semantic World Modeling
Semantics
Tracking
Title Semantic Relational Object Tracking
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