19,984 research outputs found
Hidden-Markov-Models-Based Dynamic Hand Gesture Recognition
This paper is concerned with the recognition of dynamic hand gestures. A method based on Hidden Markov Models (HMMs) is presented for dynamic gesture trajectory modeling and recognition. Adaboost algorithm is used to detect the user's hand and a contour-based hand tracker is formed combining condensation and partitioned sampling. Cubic B-spline is adopted to approximately fit the trajectory points into a curve. Invariant curve moments as global features and orientation as local features are computed to represent the trajectory of hand gesture. The proposed method can achieve automatic hand gesture online recognition and can successfully reject atypical gestures. The experimental results show that the proposed algorithm can reach better recognition results than the traditional hand recognition method
Partially hidden Markov models for privacy-preserving modeling of indoor trajectories
Markov models are natural tools for modeling trajectories, following the principle that recent location history is predictive of near-future directions. In this work we study Markov models for describing and predicting human movement in indoor spaces, with the goal of modeling the movement on a coarse scale to protect the privacy of the individuals. Modern positioning devices, however, provide location information on a much more finer scale. To utilize this additional information we develop a novel family of partially hidden Markov models that couple each observed state with an auxiliary side information vector characterizing the movement within the coarse grid cell. We implement the model as a non-parametric Bayesian model and demonstrate it on real-world trajectory data collected in a hypermarket.Peer reviewe
Projected and Hidden Markov Models for calculating kinetics and metastable states of complex molecules
Markov state models (MSMs) have been successful in computing metastable
states, slow relaxation timescales and associated structural changes, and
stationary or kinetic experimental observables of complex molecules from large
amounts of molecular dynamics simulation data. However, MSMs approximate the
true dynamics by assuming a Markov chain on a clusters discretization of the
state space. This approximation is difficult to make for high-dimensional
biomolecular systems, and the quality and reproducibility of MSMs has therefore
been limited. Here, we discard the assumption that dynamics are Markovian on
the discrete clusters. Instead, we only assume that the full phase- space
molecular dynamics is Markovian, and a projection of this full dynamics is
observed on the discrete states, leading to the concept of Projected Markov
Models (PMMs). Robust estimation methods for PMMs are not yet available, but we
derive a practically feasible approximation via Hidden Markov Models (HMMs). It
is shown how various molecular observables of interest that are often computed
from MSMs can be computed from HMMs / PMMs. The new framework is applicable to
both, simulation and single-molecule experimental data. We demonstrate its
versatility by applications to educative model systems, an 1 ms Anton MD
simulation of the BPTI protein, and an optical tweezer force probe trajectory
of an RNA hairpin
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
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