7,089 research outputs found
A multi-modal dance corpus for research into real-time interaction between humans in online virtual environments
We present a new, freely available, multimodal corpus for research into, amongst other areas, real-time realistic interaction between humans in online virtual environments. The specific corpus scenario focuses on an online dance class application scenario where students, with avatars driven by whatever 3D capture technology are locally available to them, can learn choerographies with teacher guidance in an online virtual ballet studio. As the data corpus is focused on this scenario, it consists of student/teacher dance choreographies concurrently captured at two different sites using a variety of media modalities, including synchronised audio rigs, multiple cameras, wearable inertial measurement devices and depth sensors. In the corpus, each of the several dancers perform a number of fixed choreographies, which are both graded according to a number of specific evaluation criteria. In addition, ground-truth dance choreography annotations are provided. Furthermore, for unsynchronised sensor modalities, the corpus also includes distinctive events for data stream synchronisation. Although the data corpus is tailored specifically for an online dance class application scenario, the data is free to download and used for any research and development purposes
DESIGN AND IMPLEMENTATION OF AN OBJECT TRACKING SYSTEM CONTROL USING PID AND MOVEMENT PREDICTION
The tracking system usually has some lack of problem, that is unstable system when the object moved so the tracking process canĂąâŹâąt define the object position well. On the other hands, when the object moves, the system canĂąâŹâąt track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control was used to improve the stability of tracking system, so that the result became more stable than before, it can be seen from errorof tracking. Otherwise, to looking for again the undetected object, a linier regression method was used in purpose to get more faster in finding the new position of a movement object that was disappear from the views of camera before . When the object on unmoved condition, the system has error value ñ15 pixel. For horizontal move condition of servo on slow motion ñ9.4 pixel, also on the fast motion, the error values is about ñ20.1 pixel. For the servo with vertical movement ,the error value is about ñ13.4 pixel for the slow motion and ñ45.7 pixel for fast motion. The process on finding the object that was disappear from the views of camera before, ñ2 second. Finally it can be concluded that the add of PID control and linear regression method, make the tracking system become more stabile and real time. Keyword : tracking object, PID control, regression method, real time system
Robust Estimation of 3D Human Poses from a Single Image
Human pose estimation is a key step to action recognition. We propose a
method of estimating 3D human poses from a single image, which works in
conjunction with an existing 2D pose/joint detector. 3D pose estimation is
challenging because multiple 3D poses may correspond to the same 2D pose after
projection due to the lack of depth information. Moreover, current 2D pose
estimators are usually inaccurate which may cause errors in the 3D estimation.
We address the challenges in three ways: (i) We represent a 3D pose as a linear
combination of a sparse set of bases learned from 3D human skeletons. (ii) We
enforce limb length constraints to eliminate anthropomorphically implausible
skeletons. (iii) We estimate a 3D pose by minimizing the -norm error
between the projection of the 3D pose and the corresponding 2D detection. The
-norm loss term is robust to inaccurate 2D joint estimations. We use the
alternating direction method (ADM) to solve the optimization problem
efficiently. Our approach outperforms the state-of-the-arts on three benchmark
datasets
A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
In this paper, we present a method to utilize 2D-2D point matches between
images taken during different image conditions to train a convolutional neural
network for semantic segmentation. Enforcing label consistency across the
matches makes the final segmentation algorithm robust to seasonal changes. We
describe how these 2D-2D matches can be generated with little human interaction
by geometrically matching points from 3D models built from images. Two
cross-season correspondence datasets are created providing 2D-2D matches across
seasonal changes as well as from day to night. The datasets are made publicly
available to facilitate further research. We show that adding the
correspondences as extra supervision during training improves the segmentation
performance of the convolutional neural network, making it more robust to
seasonal changes and weather conditions.Comment: In Proc. CVPR 201
The TRECVID 2007 BBC rushes summarization evaluation pilot
This paper provides an overview of a pilot evaluation of
video summaries using rushes from several BBC dramatic series. It was carried out under the auspices of TRECVID.
Twenty-two research teams submitted video summaries of
up to 4% duration, of 42 individual rushes video files aimed
at compressing out redundant and insignificant material.
The output of two baseline systems built on straightforward
content reduction techniques was contributed by Carnegie
Mellon University as a control. Procedures for developing
ground truth lists of important segments from each video
were developed at Dublin City University and applied to
the BBC video. At NIST each summary was judged by
three humans with respect to how much of the ground truth
was included, how easy the summary was to understand,
and how much repeated material the summary contained.
Additional objective measures included: how long it took
the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the
summary's duration was. Assessor agreement on finding desired segments averaged 78% and results indicate that while it is difficult to exceed the performance of baselines, a few systems did
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