  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 95% 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 640x480 pixels.
