# Mean of Means: Human Localization with Calibration-free and Unconstrained Camera Settings (extended version)

arXiv: https://arxiv.org/abs/2502.13017 · HTML: https://mlsanity.com/paper/2502.13017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 18 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video](https://mlsanity.com/paper/1908.10553/) (2019)
- [Learning Depth from Monocular Videos using Direct Methods](https://mlsanity.com/paper/1712.00175/) (2017)
- [Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors](https://mlsanity.com/paper/2103.17265/) (2021)
- [Deep Depth Completion of a Single RGB-D Image](https://mlsanity.com/paper/1803.09326/) (2018)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Multiview Detection with Feature Perspective Transformation](https://mlsanity.com/paper/2007.07247/) (2020)
- [PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modeling](https://mlsanity.com/paper/2304.13006/) (2023)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [Monocular Depth Estimation: A Survey](https://mlsanity.com/paper/1901.09402/) (2019)
- [HSC4D: Human-centered 4D Scene Capture in Large-scale Indoor-outdoor Space Using Wearable IMUs and LiDAR](https://mlsanity.com/paper/2203.09215/) (2022)
- [Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views](https://mlsanity.com/paper/2309.11962/) (2023)
- [Large-Scale Person Detection and Localization using Overhead Fisheye Cameras](https://mlsanity.com/paper/2307.08252/) (2023)

## Abstract (arXiv)

Accurate human localization is crucial for various applications, especially in the Metaverse era. Existing high precision solutions rely on expensive, tag-dependent hardware, while vision-based methods offer a cheaper, tag-free alternative. However, current vision solutions based on stereo vision face limitations due to rigid perspective transformation principles and error propagation in multi-stage SVD solvers. These solutions also require multiple high-resolution cameras with strict setup constraints.To address these limitations, we propose a probabilistic approach that considers all points on the human body as observations generated by a distribution centered around the body's geometric center. This enables us to improve sampling significantly, increasing the number of samples for each point of interest from hundreds to billions. By modeling the relation between the means of the distributions of world coordinates and pixel coordinates, leveraging the Central Limit Theorem, we ensure normality and facilitate the learning process. Experimental results demonstrate human localization accuracy of 96% within a 0.3m range and nearly 100% accuracy within a 0.5m range, achieved at a low cost of only 10 USD using two web cameras with a resolution of 640×480 pixels.