# A Robust Filter for Marker-less Multi-person Tracking in Human-Robot Interaction Scenarios

arXiv: https://arxiv.org/abs/2406.01832 · HTML: https://mlsanity.com/paper/2406.01832/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [VoxelTrack: Multi-Person 3D Human Pose Estimation and Tracking in the Wild](https://mlsanity.com/paper/2108.02452/) (2021)
- [2D Human Pose Estimation: A Survey](https://mlsanity.com/paper/2204.07370/) (2022)
- [Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction](https://mlsanity.com/paper/2310.03314/) (2023)
- [Human-Robot Collaborative Carrying of Objects with Unknown Deformation Characteristics](https://mlsanity.com/paper/2201.10392/) (2022)
- [Object Permanence Filter for Robust Tracking with Interactive Robots](https://mlsanity.com/paper/2403.08231/) (2024)

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

- [COMETH: Convex Optimization for Multiview Estimation and Tracking of Humans](https://mlsanity.com/paper/2508.20920/) (2025)

## Abstract (arXiv)

Pursuing natural and marker-less human-robot interaction (HRI) has been a long-standing robotics research focus, driven by the vision of seamless collaboration without physical markers. Marker-less approaches promise an improved user experience, but state-of-the-art struggles with the challenges posed by intrinsic errors in human pose estimation (HPE) and depth cameras. These errors can lead to issues such as robot jittering, which can significantly impact the trust users have in collaborative systems. We propose a filtering pipeline that refines incomplete 3D human poses from an HPE backbone and a single RGB-D camera to address these challenges, solving for occlusions that can degrade the interaction. Experimental results show that using the proposed filter leads to more consistent and noise-free motion representation, reducing unexpected robot movements and enabling smoother interaction.