# Semantic Relational Object Tracking

## TL;DR

This paper presents a novel probabilistic framework for semantic object tracking that combines bottom-up anchoring with high-level reasoning to improve tracking robustness in complex, real-world scenarios.

## Contribution

It introduces a new anchoring matching function and integrates it with a probabilistic tracker for enhanced semantic object tracking.

## Key findings

- Effective in tracking occluded objects
- Maintains object relations through probabilistic reasoning
- Validated on real-world annotated data

## 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 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.