2,745 research outputs found
Cascaded 3D Full-body Pose Regression from Single Depth Image at 100 FPS
There are increasing real-time live applications in virtual reality, where it
plays an important role in capturing and retargetting 3D human pose. But it is
still challenging to estimate accurate 3D pose from consumer imaging devices
such as depth camera. This paper presents a novel cascaded 3D full-body pose
regression method to estimate accurate pose from a single depth image at 100
fps. The key idea is to train cascaded regressors based on Gradient Boosting
algorithm from pre-recorded human motion capture database. By incorporating
hierarchical kinematics model of human pose into the learning procedure, we can
directly estimate accurate 3D joint angles instead of joint positions. The
biggest advantage of this model is that the bone length can be preserved during
the whole 3D pose estimation procedure, which leads to more effective features
and higher pose estimation accuracy. Our method can be used as an
initialization procedure when combining with tracking methods. We demonstrate
the power of our method on a wide range of synthesized human motion data from
CMU mocap database, Human3.6M dataset and real human movements data captured in
real time. In our comparison against previous 3D pose estimation methods and
commercial system such as Kinect 2017, we achieve the state-of-the-art
accuracy
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books
We develop a large-scale deep learning model to predict price movements from
limit order book (LOB) data of cash equities. The architecture utilises
convolutional filters to capture the spatial structure of the limit order books
as well as LSTM modules to capture longer time dependencies. The proposed
network outperforms all existing state-of-the-art algorithms on the benchmark
LOB dataset [1]. In a more realistic setting, we test our model by using one
year market quotes from the London Stock Exchange and the model delivers a
remarkably stable out-of-sample prediction accuracy for a variety of
instruments. Importantly, our model translates well to instruments which were
not part of the training set, indicating the model's ability to extract
universal features. In order to better understand these features and to go
beyond a "black box" model, we perform a sensitivity analysis to understand the
rationale behind the model predictions and reveal the components of LOBs that
are most relevant. The ability to extract robust features which translate well
to other instruments is an important property of our model which has many other
applications.Comment: 12 pages, 9 figure
- …