# Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

arXiv: https://arxiv.org/abs/2203.06911 · HTML: https://mlsanity.com/paper/2203.06911/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2022.

## Built on by (most cited first, 1 of 1)

- [Kairos: Grounded Forecasting of Presence and Directional Flow in 4D Scene Graphs](https://mlsanity.com/paper/2609.27467/) (2026)

## Abstract (arXiv)

This work presents a non-parametric spatio-temporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multi-week data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.