# Real-time Tracking-by-Detection of Human Motion in RGB-D Camera Networks

## TL;DR

This paper introduces a real-time multi-camera system that improves human body pose estimation by fusing data with a Kalman filter and a hierarchical body model, demonstrating superior accuracy and flexibility in diverse scenarios.

## Contribution

The novel system combines a Kalman filter with a hierarchical body model for real-time, distributed human motion tracking without restrictions on camera setup or number of persons.

## Key findings

- Outperforms state-of-the-art methods in accuracy
- Works with arbitrary camera configurations
- Operates in real-time for practical applications

## Abstract

This paper presents a novel real-time tracking system capable of improving body pose estimation algorithms in distributed camera networks. The first stage of our approach introduces a linear Kalman filter operating at the body joints level, used to fuse single-view body poses coming from different detection nodes of the network and to ensure temporal consistency between them. The second stage, instead, refines the Kalman filter estimates by fitting a hierarchical model of the human body having constrained link sizes in order to ensure the physical consistency of the tracking. The effectiveness of the proposed approach is demonstrated through a broad experimental validation, performed on a set of sequences whose ground truth references are generated by a commercial marker-based motion capture system. The obtained results show how the proposed system outperforms the considered state-of-the-art approaches, granting accurate and reliable estimates. Moreover, the developed methodology constrains neither the number of persons to track, nor the number, position, synchronization, frame-rate, and manufacturer of the RGB-D cameras used. Finally, the real-time performances of the system are of paramount importance for a large number of real-world applications.