Semantic relational object tracking

23Citations
Citations of this article
31Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Persson, A., Zuidberg Dos Martires, P., De Raedt, L., & Loutfi, A. (2020). Semantic relational object tracking. IEEE Transactions on Cognitive and Developmental Systems, 12(1), 84–97. https://doi.org/10.1109/TCDS.2019.2915763

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free