6,278 research outputs found
Consistent forcing scheme in the cascaded lattice Boltzmann method
In this paper, we give a more pellucid derivation for the cascaded lattice
Boltzmann method (CLBM) based on a general multiple-relaxation-time (MRT) frame
through defining a shift matrix. When the shift matrix is a unit matrix, the
CLBM degrades into an MRT LBM. Based on this, a consistent forcing scheme is
developed for the CLBM. The applicability of the non-slip rule, the
second-order convergence rate in space and the property of isotropy for the
consistent forcing scheme is demonstrated through the simulation of several
canonical problems. Several other existing force schemes previously used in the
CLBM are also examined. The study clarifies the relation between MRT LBM and
CLBM under a general framework
Unsupervised Learning of Long-Term Motion Dynamics for Videos
We present an unsupervised representation learning approach that compactly
encodes the motion dependencies in videos. Given a pair of images from a video
clip, our framework learns to predict the long-term 3D motions. To reduce the
complexity of the learning framework, we propose to describe the motion as a
sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent
Neural Network based Encoder-Decoder framework to predict these sequences of
flows. We argue that in order for the decoder to reconstruct these sequences,
the encoder must learn a robust video representation that captures long-term
motion dependencies and spatial-temporal relations. We demonstrate the
effectiveness of our learned temporal representations on activity
classification across multiple modalities and datasets such as NTU RGB+D and
MSR Daily Activity 3D. Our framework is generic to any input modality, i.e.,
RGB, Depth, and RGB-D videos.Comment: CVPR 201
Increasing Compression Ratio of Low Complexity Compressive Sensing Video Encoder with Application-Aware Configurable Mechanism
With the development of embedded video acquisition nodes and wireless video
surveillance systems, traditional video coding methods could not meet the needs
of less computing complexity any more, as well as the urgent power consumption.
So, a low-complexity compressive sensing video encoder framework with
application-aware configurable mechanism is proposed in this paper, where novel
encoding methods are exploited based on the practical purposes of the real
applications to reduce the coding complexity effectively and improve the
compression ratio (CR). Moreover, the group of processing (GOP) size and the
measurement matrix size can be configured on the encoder side according to the
post-analysis requirements of an application example of object tracking to
increase the CR of encoder as best as possible. Simulations show the proposed
framework of encoder could achieve 60X of CR when the tracking successful rate
(SR) is still keeping above 90%.Comment: 5 pages with 6figures and 1 table,conferenc
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