Semantic Relational Object Tracking
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| Title: | Semantic Relational Object Tracking |
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| Authors: | Persson, Andreas, 1980, Zuidberg Dos Martires, Pedro, Loutfi, Amy, 1978, De Raedt, Luc |
| Source: | IEEE Transactions on Cognitive and Developmental Systems. 12(1):84-97 |
| Subject Terms: | Semantic World Modeling, Perceptual Anchoring, Probabilistic Reasoning, Probabilistic Logic Programming, Object Tracking, Relational Particle Filtering |
| Description: | 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. |
| File Description: | |
| Access URL: | https://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-73529 https://doi.org/10.1109/TCDS.2019.2915763 |
| Database: | SwePub |
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Nájsť tento článok vo Web of Science | 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. |
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| ISSN: | 23798920 |
| DOI: | 10.1109/TCDS.2019.2915763 |