Real-time Tracking-by-Detection of Human Motion in RGB-D Camera Networks
This work addresses the problem of reliable and scalable human tracking for applications like surveillance or human-computer interaction, though it appears incremental by building on existing tracking-by-detection and filtering techniques.
The paper tackles real-time human motion tracking in distributed RGB-D camera networks by fusing single-view poses with a Kalman filter and refining them with a constrained body model, achieving improved accuracy over state-of-the-art methods as validated against marker-based ground truth.
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.