Semantic Relational Object Tracking
This work addresses the problem of robust object tracking in complex, occluded environments for applications in robotics and computer vision, presenting an incremental improvement by combining existing techniques.
The paper tackles the problem of semantic world modeling by integrating probabilistic reasoning with object anchoring to track objects through occlusions and complex scenarios, demonstrating performance through scenarios like the shell game where anchored objects are retained by preserving relations.
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.