22,334 research outputs found
Adaptive appearance learning for visual object tracking
This paper addresses online learning of reference object distribution in the context of two hybrid tracking schemes that combine the mean shift with local point feature correspondences, and the mean shift under the Bayesian framework, respectively. The reference object distribution is built up by a kernel-weighted color histogram. The main contributions of the proposed schemes includes: (a) an adaptive learning strategy that seeks to update the reference object distribution when the changes are caused by the intrinsic object dynamic without partial occlusion/ intersection; (b) novel dynamic maintenance of object feature points by exploring both foreground and background sets; (c) integration of adaptive appearance and local point features in joint object appearance similarity and local point features correspondences-based tracker to improve [7]; (d) integration of adaptive appearance in joint
appearance similarity and particle filter tracker under the
Bayesian framework to improve [10]. Experimental results on a range of videos captured by a dynamic/stationary camera
demonstrate the effectiveness of the proposed schemes in terms of robustness to partial occlusions, tracking drifts and tightness and accuracy of tracked bounding box. Comparisons are also made with the two hybrid trackers together with 3 existing trackers
Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation
This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator
Neural Filters for Jet Analysis
We study the efficiency of a neural-net filter and deconvolution method for
estimating jet energies and spectra in high-background reactions such as
nuclear collisions at the relativistic heavy-ion collider and the large hadron
collider. The optimal network is shown to be surprisingly close but not
identical to a linear high-pass filter. A suitably constrained deconvolution
method is shown to uncover accurately the underlying jet distribution in spite
of the broad network response. Finally, we show that possible changes of the
jet spectrum in nuclear collisions can be analyzed quantitatively, in terms of
an effective energy loss with the proposed method. {} {Dong D W and Gyulassy M
1993}{Neural filters for jet analysis}
{(LBL-31560) Physical Review E Vol~47(4) pp~2913-2922}Comment: 21 pages of Postscript, (LBL-31560
Adaptive channel selection for DOA estimation in MIMO radar
We present adaptive strategies for antenna selection for Direction of Arrival
(DoA) estimation of a far-field source using TDM MIMO radar with linear arrays.
Our treatment is formulated within a general adaptive sensing framework that
uses one-step ahead predictions of the Bayesian MSE using a parametric family
of Weiss-Weinstein bounds that depend on previous measurements. We compare in
simulations our strategy with adaptive policies that optimize the Bobrovsky-
Zaka{\i} bound and the Expected Cram\'er-Rao bound, and show the performance
for different levels of measurement noise.Comment: Submitted to the 25th European Signal Processing Conference
(EUSIPCO), 201
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
A chaotic spread spectrum system for underwater acoustic communication
The work is supported in part by NSFC (Grant no. 61172070), IRT of Shaanxi Province (2013KCT-04), EPSRC (Grant no.Ep/1032606/1).Peer reviewedPostprin
Particle Metropolis-Hastings using gradient and Hessian information
Particle Metropolis-Hastings (PMH) allows for Bayesian parameter inference in
nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and
particle filtering. The latter is used to estimate the intractable likelihood.
In its original formulation, PMH makes use of a marginal MCMC proposal for the
parameters, typically a Gaussian random walk. However, this can lead to a poor
exploration of the parameter space and an inefficient use of the generated
particles.
We propose a number of alternative versions of PMH that incorporate gradient
and Hessian information about the posterior into the proposal. This information
is more or less obtained as a byproduct of the likelihood estimation. Indeed,
we show how to estimate the required information using a fixed-lag particle
smoother, with a computational cost growing linearly in the number of
particles. We conclude that the proposed methods can: (i) decrease the length
of the burn-in phase, (ii) increase the mixing of the Markov chain at the
stationary phase, and (iii) make the proposal distribution scale invariant
which simplifies tuning.Comment: 27 pages, 5 figures, 2 tables. The final publication is available at
Springer via: http://dx.doi.org/10.1007/s11222-014-9510-
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