This video accompanies the following publication:
D. Michel, A. Qammaz and A.A. Argyros, “Markerless 3D Human Pose Estimation and Tracking based on RGBD Cameras: an Experimental Evaluation”, in International Conference on Pervasive Technologies Related to Assistive Environments (PETRA 2017), Rhodes, Greece, June 2017.
It demonstrates the use of the FORTH Human Body Tracker (FHBT) to support the teleoperation of a NAO robot.
The paper itself presents a comparative experimental evaluation of three methods that estimate the 3D position, orientation and articulation of the human body from markerless visual observations obtained by RGBD cameras. The evaluated methods are representatives of three broad 3D human pose estimation/tracking methods. Specifically, the first is the discriminative approach adopted by OpenNI. The second is a hybrid approach that depends on the input of two synchronized and extrinsically calibrated RGBD cameras. Finally, the third one is the generative FHBT method that depends on input provided by a single RGBD camera. The experimental evaluation of these methods has been based on a
publicly available data set that is annotated with ground truth. The obtained results expose the characteristics of the three methods and provide evidence that can guide the selection of the most appropriate one depending on the requirements of a certain application domain.
The video also shows teleoperation of the head of NAO based on the vision-based head pose estimation method presented in the following publication:
P. Padeleris, X. Zabulis and A.A. Argyros, “Head pose estimation on depth data based on Particle Swarm Optimization”, In IEEE Computer Vision and Pattern Recognition Workshops (CVPRW 2012), IEEE, pp. 42-49, Providence, Rhode Island, USA, June 2012.