Markerless Motion Capture in the Crowd

This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

Automated Markerless Extraction of Walking People Using Deformable Contour Models

We develop a new automated markerless motion capture system for the analysis of walking people. We employ global evidence gathering techniques guided by biomechanical analysis to robustly extract articulated motion. This forms a basis for new deformable contour models, using local image cues to capture shape and motion at a more detailed level. We extend the greedy snake formulation to include temporal constraints and occlusion modelling, increasing the capability of this technique when dealing with cluttered and self-occluding extraction targets. This approach is evaluated on a large database of indoor and outdoor video data, demonstrating fast and autonomous motion capture for walking people

Markerless Facial Motion Capture

With the ever-rising capabilities of motion capture systems; this project explored markerless facial motion capture programs using the Kinect Sensor for Xbox. Many systems today still use markers and end up retargeting after a motion capture recording. This project used a simpler process of setting up and being able to display the effects live. An off-the-shelf system was built using a computer, a Kinect Sensor, a plug-in from Brekel, and Autodesk software. The first goal was to create a process that was able to capture and project live facial motion for fewer than $500 USD. Anything over$500 USD was considered to be more of a professional studio set-up. With an inexpensive setup, amateur users can do motion capture outside of a studio. The second goal was to observe the outcome of the audiences\u27 responses and see if interaction felt more mechanical than human

Integration stabilometry with a markerless motion capture

Стабилометрия позволяет оценить: работу двигательной и нервной системы пациента,координационные способности, различные нарушения опоры и баланса, тремор конечностей, а также выявить нарушенные нервные связи, патологии вестибулярного аппарата и т. д. В ходе работы будет проведена интерпретация полученных данных с помощью стабилометрических методов, что позволит поставить точный диагноз на ранней стадии развития отклонений. Также в дополнении с данным методом будет использоваться устройство видиозахвата, в итоге сумма качество диагностики возрастет.Stabilometry allows to evaluate: the muscle and the nervous system of the patient, coordination ability, various disorders of support and balance, tremor of the extremities, and to identify disrupted neural pathways, pathology of the vestibular apparatus, etc. In the course of scientific work will be carried out the interpretation of the obtained data with the help of stabilometric methods that allow an accurate diagnosis at an early stage of development deviations. Also, in addition with this method, you will use the device videozakhvat, in the end, the sum of the quality of diagnosis will increase

Parallelization Strategies for Markerless Human Motion Capture

Markerless Motion Capture (MMOCAP) is the problem of determining the pose of a person from images captured by one or several cameras simultaneously without using markers on the subject. Evaluation of the solutions is frequently the most time-consuming task, making most of the proposed methods inapplicable in real-time scenarios. This paper presents an efficient approach to parallelize the evaluation of the solutions in CPUs and GPUs. Our proposal is experimentally compared on six sequences of the HumanEva-I dataset using the CMAES algorithm. Multiple algorithm’s configurations were tested to analyze the best trade-off in regard to the accuracy and computing time. The proposed methods obtain speedups of 8× in multi-core CPUs, 30× in a single GPU and up to 110× using 4 GPU

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

A Portable, Low-Cost Wheelchair Ergometer Design Based on a Mathematical Model of Pediatric Wheelchair Dynamics

Evaluation and training of wheelchair propulsion improves efficiency and prevents orthopaedic injury in pediatric manual wheelchair users. Ergometers allow static propulsion and emulate typical conditions. Currently available ergometers have deficiencies that limit their use in motion analysis. A new ergometer is developed and evaluated based on a model of wheelchair inertial dynamics that eliminates these deficiencies. This makes integrated motion analysis of wheelchair propulsion in current community, home, and international outreach efforts possible

Markerless View Independent Gait Analysis with Self-camera Calibration

We present a new method for viewpoint independent markerless gait analysis. The system uses a single camera, does not require camera calibration and works with a wide range of directions of walking. These properties make the proposed method particularly suitable for identification by gait, where the advantages of completely unobtrusiveness, remoteness and covertness of the biometric system preclude the availability of camera information and use of marker based technology. Tests on more than 200 video sequences with subjects walking freely along different walking directions have been performed. The obtained results show that markerless gait analysis can be achieved without any knowledge of internal or external camera parameters and that the obtained data that can be used for gait biometrics purposes. The performance of the proposed method is particularly encouraging for its appliance in surveillance scenarios