Clinical Patient Tracking in the Presence of Transient and Permanent Occlusions via Geodesic Feature

TL;DR

This paper introduces a geodesic surface feature and multi-hypothesis tracking framework to enhance human motion tracking accuracy with RGB-D cameras, especially under challenging occlusion conditions during clinical rehabilitation.

Contribution

It proposes a novel geodesic distance-based feature combined with multi-hypothesis tracking to improve robustness against occlusions and surface deformations in human motion tracking.

Findings

Achieves robustness to surface deformations.

Handles transient occlusions effectively.

Outperforms existing tracking methods in simulated occlusion scenarios.

Abstract

This paper develops a method to use RGB-D cameras to track the motions of a human spinal cord injury patient undergoing spinal stimulation and physical rehabilitation. Because clinicians must remain close to the patient during training sessions, the patient is usually under permanent and transient occlusions due to the training equipment and the movements of the attending clinicians. These occlusions can significantly degrade the accuracy of existing human tracking methods. To improve the data association problem in these circumstances, we present a new global feature based on the geodesic distances of surface mesh points to a set of anchor points. Transient occlusions are handled via a multi-hypothesis tracking framework. To evaluate the method, we simulated different occlusion sizes on a data set captured from a human in varying movement patterns, and compared the proposed feature with other tracking methods. The results show that the proposed method achieves robustness to both surface deformations and transient occlusions.