34,292 research outputs found
3D head tracking using normal flow constraints in a vehicle environment
Head tracking is a key component in applications such as human computer interaction, person monitoring, driver monitoring, video conferencing, and object-based compression. The motion of a driverâs head can tell us a lot about his/her mental state; e.g. whether he/she is drowsy, alert, aggressive,
comfortable, tense, distracted, etc. This paper reviews an optical flow based method to track the head pose, both orientation and position, of a person and presents results from real world data recorded in a car environment
Perturbative Analysis of the Two-body Problem in (2+1)-AdS gravity
We derive a perturbative scheme to treat the interaction between point
sources and AdS-gravity. The interaction problem is equivalent to the search of
a polydromic mapping , endowed with 0(2,2) monodromies,
between the physical coordinate system and a Minkowskian 4-dimensional
coordinate system, which is however constrained to live on a hypersurface. The
physical motion of point sources is therefore mapped to a geodesic motion on
this hypersuface. We impose an instantaneous gauge which induces a set of
equations defining such a polydromic mapping. Their consistency leads naturally
to the Einstein equations in the same gauge. We explore the restriction of the
monodromy group to O(2,1), and we obtain the solution of the fields
perturbatively in the cosmological constant.Comment: 19 pages, no figures, LaTeX fil
On using gait to enhance frontal face extraction
Visual surveillance finds increasing deployment formonitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose. In surveillance environments, it is necessary to handle pose variation of the human head, low frame rate, and low resolution input images. We describe the first use of gait to enable face acquisition and recognition, by analysis of 3-D head motion and gait trajectory, with super-resolution analysis. We use region- and distance-based refinement of head pose estimation. We develop a direct mapping to relate the 2-D image with a 3-D model. In gait trajectory analysis, we model the looming effect so as to obtain the correct face region. Based on head position and the gait trajectory, we can reconstruct high-quality frontal face images which are demonstrated to be suitable for face recognition. The contributions of this research include the construction of a 3-D model for pose estimation from planar imagery and the first use of gait information to enhance the face extraction process allowing for deployment in surveillance scenario
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
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