User recognition for guiding and following people with a mobile robot in a clinical environment

Rehabilitative follow-up care is important for stroke patients to regain their motor and cognitive skills. We aim to develop a robotic rehabilitation assistant for walking exercises in late stages of rehabilitation. The robotic rehab assistant is to accompany inpatients during their self-training, practicing both mobility and spatial orientation skills. To hold contact to the patient, even after temporally full occlusions, robust user re-identification is essential. Therefore, we implemented a person re-identification module that continuously re-identifies the patient, using only few amount of the robot's processing resources. It is robust to varying illumination and occlusions. State-of-the-art performance is confirmed on a standard benchmark dataset, as well as on a recorded scenario-specific dataset. Additionally, the benefit of using a visual re-identification component is verified by live-tests with the robot in a stroke rehab clinic.

[1]  Bingpeng Ma,et al.  Local Descriptors Encoded by Fisher Vectors for Person Re-identification , 2012, ECCV Workshops.

[2]  Peter J. Kyberd,et al.  Bridging the gap between robotic technology and health care , 2014, Biomed. Signal Process. Control..

[3]  Horst-Michael Groß,et al.  APFel: The intelligent video analysis and surveillance system for assisting human operators , 2013, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[4]  Horst-Michael Groß,et al.  MIRA - middleware for robotic applications , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Xiaogang Wang,et al.  Unsupervised Salience Learning for Person Re-identification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Vittorio Murino,et al.  Custom Pictorial Structures for Re-identification , 2011, BMVC.

[7]  Christian Vollmer,et al.  Estimation of human upper body orientation for mobile robotics using an SVM decision tree on monocular images , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Shaogang Gong,et al.  Person re-identification by probabilistic relative distance comparison , 2011, CVPR 2011.

[9]  Bingpeng Ma,et al.  BiCov: a novel image representation for person re-identification and face verification , 2012, BMVC.

[10]  Horst-Michael Groß,et al.  View Invariant Appearance-Based Person Reidentification Using Fast Online Feature Selection and Score Level Fusion , 2012, 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance.

[11]  Alessandro Perina,et al.  Person re-identification by symmetry-driven accumulation of local features , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Horst-Michael Groß,et al.  Vision-based hyper-real-time object tracker for robotic applications , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Horst-Michael Groß,et al.  Mobile Robotic Rehabilitation Assistant for walking and orientation training of Stroke Patients: A report on work in progress , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[14]  Fei Xiong,et al.  Person Re-Identification Using Kernel-Based Metric Learning Methods , 2014, ECCV.

[15]  Horst-Michael Groß,et al.  Evaluation of multi feature fusion at score-level for appearance-based person re-identification , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[16]  Horst-Michael Groß,et al.  People detection and distinction of their walking aids in 2D laser range data based on generic distance-invariant features , 2014, The 23rd IEEE International Symposium on Robot and Human Interactive Communication.

[17]  Shaogang Gong,et al.  Person Re-Identification by Support Vector Ranking , 2010, BMVC.

[18]  Horst-Michael Groß,et al.  People Tracking on a Mobile Companion Robot , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[19]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .