# Semantic Relational Object Tracking

arXiv: https://arxiv.org/abs/1902.09937 · HTML: https://mlsanity.com/paper/1902.09937/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 0 of its 42 references that are in the map.
- First posted on arXiv 26 Feb 2019.

## Placed in

- World models / [Latent predictive world models](https://mlsanity.com/wm/latent-predictive-world-models/)

## Built on by (most cited first, 8 of 8)

- [Boosting Object Representation Learning via Motion and Object Continuity](https://mlsanity.com/paper/2211.09771/) (2022)
- [Semirings for Probabilistic and Neuro-Symbolic Logic Programming](https://mlsanity.com/paper/2402.13782/) (2024)
- [Symbolic Learning and Reasoning with Noisy Data for Probabilistic Anchoring](https://mlsanity.com/paper/2002.10373/) (2020)
- [Online Grounding of Symbolic Planning Domains in Unknown Environments](https://mlsanity.com/paper/2112.10007/) (2021)
- [Towards Two-Stream Foveation-based Active Vision Learning](https://mlsanity.com/paper/2403.15977/) (2024)
- [Maintaining a Reliable World Model using Action-aware Perceptual Anchoring](https://mlsanity.com/paper/2107.03038/) (2021)
- [LAP: A Language-Aware Planning Model For Procedure Planning In Instructional Videos](https://mlsanity.com/paper/2603.09743/) (2026)
- [Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking](https://mlsanity.com/paper/2211.02760/) (2022)

## Abstract (arXiv)

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 the rich continuous data from perceptual sensor data. A novel anchoring matching function method 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.