1,263 research outputs found
Remote Heart Rate Estimation Using Consumer-Grade Cameras
There are many ways in which the remote non-contact detection of the human heart rate might be useful. This is especially true if it can be done using inexpensive equipment such as consumer-grade cameras. Many studies and experiments have been performed in recent years to help reliably determine the heart rate from video footage of a person. The methods have taken an analysis approach which involves temporal Itering and frequency spectrum examination. This study attempts to answer questions about the noise sources which inhibit these methods from estimating the heart rate. Other statistical processes are examined for their use in reducing the noise in the system. Methods for locating the skin of a moving individual are explored and used with the purpose for acquiring the heart rate. Alternative methods borrowed from other fields are also introduced to find if they have merit in remote heart rate detection
Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
Sparsity-based representations have recently led to notable results in
various visual recognition tasks. In a separate line of research, Riemannian
manifolds have been shown useful for dealing with features and models that do
not lie in Euclidean spaces. With the aim of building a bridge between the two
realms, we address the problem of sparse coding and dictionary learning over
the space of linear subspaces, which form Riemannian structures known as
Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into
the space of symmetric matrices by an isometric mapping. This in turn enables
us to extend two sparse coding schemes to Grassmann manifolds. Furthermore, we
propose closed-form solutions for learning a Grassmann dictionary, atom by
atom. Lastly, to handle non-linearity in data, we extend the proposed Grassmann
sparse coding and dictionary learning algorithms through embedding into Hilbert
spaces.
Experiments on several classification tasks (gender recognition, gesture
classification, scene analysis, face recognition, action recognition and
dynamic texture classification) show that the proposed approaches achieve
considerable improvements in discrimination accuracy, in comparison to
state-of-the-art methods such as kernelized Affine Hull Method and
graph-embedding Grassmann discriminant analysis.Comment: Appearing in International Journal of Computer Visio
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving
Decision-making for urban autonomous driving is challenging due to the
stochastic nature of interactive traffic participants and the complexity of
road structures. Although reinforcement learning (RL)-based decision-making
scheme is promising to handle urban driving scenarios, it suffers from low
sample efficiency and poor adaptability. In this paper, we propose Scene-Rep
Transformer to improve the RL decision-making capabilities with better scene
representation encoding and sequential predictive latent distillation.
Specifically, a multi-stage Transformer (MST) encoder is constructed to model
not only the interaction awareness between the ego vehicle and its neighbors
but also intention awareness between the agents and their candidate routes. A
sequential latent Transformer (SLT) with self-supervised learning objectives is
employed to distill the future predictive information into the latent scene
representation, in order to reduce the exploration space and speed up training.
The final decision-making module based on soft actor-critic (SAC) takes as
input the refined latent scene representation from the Scene-Rep Transformer
and outputs driving actions. The framework is validated in five challenging
simulated urban scenarios with dense traffic, and its performance is manifested
quantitatively by the substantial improvements in data efficiency and
performance in terms of success rate, safety, and efficiency. The qualitative
results reveal that our framework is able to extract the intentions of neighbor
agents to help make decisions and deliver more diversified driving behaviors
- …