Papers › Bayesian Online Learning for Human-assisted Target Localization
Bayesian Online Learning for Human-assisted Target Localization
Min-Won Seo, Solmaz S. Kia
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
We consider a human-assisted autonomy sensor fusion for dynamic target localization in a Bayesian framework. Autonomous sensor-based tracking systems can suffer from observability and target detection failure. Humans possess valuable qualitative information derived from their past knowledge and rapid situational awareness that can give them an advantage over machine perception in many scenarios. To compensate for the shortcomings of an autonomous tracking system, we propose to collect spatial sensing information from human operators who visually monitor the target and can provide target localization information in the form of free sketches encircling the area where the target is located. However, human inputs cannot be taken deterministically and trusted absolutely due to their inherent subjectivity and variability. Our focus in this paper is to construct an adaptive probabilistic model for human-provided inputs where the adaptation terms capture the level of reliability of the human inputs. The next contribution of this paper is a novel joint Bayesian learning method to fuse human and autonomous sensor inputs in a manner that the dynamic changes in human detection reliability are also captured and accounted for. Unlike deep learning frameworks, a unique aspect of this Bayesian modeling framework is its analytical closed-form update equations. This feature provides computational efficiency and allows for online learning from limited data sets. Simulations demonstrate our results, underscoring the value of human-machine collaboration in autonomous systems.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections