Markerless Human Motion Capture for Gait Analysis

The aim of our study is to detect balance disorders and a tendency towards the falls in the elderly, knowing gait parameters. In this paper we present a new tool for gait analysis based on markerless human motion capture, from camera feeds. The system introduced here, recovers the 3D positions of several key points of the human body while walking. Foreground segmentation, an articulated body model and particle filtering are basic elements of our approach. No dynamic model is used thus this system can be described as generic and simple to implement. A modified particle filtering algorithm, which we call Interval Particle Filtering, is used to reorganise and search through the model's configurations search space in a deterministic optimal way. This algorithm was able to perform human movement tracking with success. Results from the treatment of a single cam feeds are shown and compared to results obtained using a marker based human motion capture system

Models and estimators for markerless human motion tracking

In this work, we analyze the diferent components of a model-based motion tracking system. The system consists in: a human body model, an estimator, and a likelihood or cost function

Visual Perception for Manipulation and Imitation in Humanoid Robots

This thesis deals with visual perception for manipulation and imitation in humanoid robots. In particular, real-time applicable methods for object recognition and pose estimation as well as for markerless human motion capture have been developed. As only sensor a small baseline stereo camera system (approx. human eye distance) was used. An extensive experimental evaluation has been performed on simulated as well as real image data from real-world scenarios using the humanoid robot ARMAR-III

Real-Time Human Motion Capture with Multiple Depth Cameras

Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. We also introduce a dataset of ~6 million synthetic depth frames for pose estimation from multiple cameras and exceed state-of-the-art results on the Berkeley MHAD dataset.Comment: Accepted to computer robot vision 201

Articulated Object Tracking from Visual Sensory Data for Robotic Manipulation

Roboti juhtimine liigestatud objekti manipuleerimisel vajab robustset ja täpsetobjekti oleku hindamist. Oleku hindamise tulemust kasutatakse tagasisidena vastavate roboti liigutuste arvutamisel soovitud manipulatsiooni tulemuse saavutamiseks. Selles töös uuritakse robootilise manipuleerimise visuaalse tagasiside teostamist. Tehisnägemisele põhinevat servode liigutamist juhitakse ruutplaneerimise raamistikus võimaldamaks humanoidsel robotil läbi viia objekti manipulatsiooni. Esitletakse tehisnägemisel põhinevat liigestatud objekti oleku hindamise meetodit. Me näitame väljapakutud meetodi efektiivsust mitmel erineval eksperimendil HRP-4 humanoidse robotiga. Teeme ka ettepaneku ühendada masinõppe ja serva tuvastamise tehnikad liigestatud objekti manipuleerimise markeerimata visuaalse tagasiside teostamiseks reaalajas.In order for a robot to manipulate an articulated object, it needs to know itsstate (i.e. its pose); that is to say: where and in which configuration it is. Theresult of the object’s state estimation is to be provided as a feedback to the control to compute appropriate robot motion and achieve the desired manipulation outcome. This is the main topic of this thesis, where articulated object state estimation is solved using visual feedback. Vision based servoing is implemented in a Quadratic Programming task space control framework to enable humanoid robot to perform articulated objects manipulation. We thoroughly developed our methodology for vision based articulated object state estimation on these bases.We demonstrate its efficiency by assessing it on several real experiments involving the HRP-4 humanoid robot. We also propose to combine machine learning and edge extraction techniques to achieve markerless, realtime and robust visual feedback for articulated object manipulation

A Continuous Grasp Representation for the Imitation Learning of Grasps on Humanoid Robots

Models and methods are presented which enable a humanoid robot to learn reusable, adaptive grasping skills. Mechanisms and principles in human grasp behavior are studied. The findings are used to develop a grasp representation capable of retaining specific motion characteristics and of adapting to different objects and tasks. Based on the representation a framework is proposed which enables the robot to observe human grasping, learn grasp representations, and infer executable grasping actions

Tracking a walking person using activity-guided annealed particle filtering

Tracking human pose using observations from less than three cameras is a challenging task due to ambiguity in the available image evidence. This work presents a method for tracking using a pre-trained model of activity to guidesampling within an Annealed Particle Filtering framework. The approach is an example of model-based analysis-by-synthesis and is capable of robust tracking from less than 3 cameras with reduced numbers of samples. We test the scheme on a common dataset containing ground truth mo-tion capture data and compare against quantitative results for standard Annealed Particle Filtering. We find lower ab-solute and relative error scores for both monocular and 2-camera sequences using 80% fewer particles. © 2008 IEEE