1,499 research outputs found
Enhanced particle PHD filtering for multiple human tracking
PhD ThesisVideo-based single human tracking has found wide application but multiple
human tracking is more challenging and enhanced processing techniques are
required to estimate the positions and number of targets in each frame. In
this thesis, the particle probability hypothesis density (PHD) lter is therefore
the focus due to its ability to estimate both localization and cardinality
information related to multiple human targets. To improve the tracking performance
of the particle PHD lter, a number of enhancements are proposed.
The Student's-t distribution is employed within the state and measurement
models of the PHD lter to replace the Gaussian distribution because
of its heavier tails, and thereby better predict particles with larger amplitudes.
Moreover, the variational Bayesian approach is utilized to estimate
the relationship between the measurement noise covariance matrix and the
state model, and a joint multi-dimensioned Student's-t distribution is exploited.
In order to obtain more observable measurements, a backward retrodiction
step is employed to increase the measurement set, building upon the
concept of a smoothing algorithm. To make further improvement, an adaptive
step is used to combine the forward ltering and backward retrodiction
ltering operations through the similarities of measurements achieved over
discrete time. As such, the errors in the delayed measurements generated by
false alarms and environment noise are avoided.
In the nal work, information describing human behaviour is employed
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Abstract v
to aid particle sampling in the prediction step of the particle PHD lter,
which is captured in a social force model. A novel social force model is
proposed based on the exponential function. Furthermore, a Markov Chain
Monte Carlo (MCMC) step is utilized to resample the predicted particles,
and the acceptance ratio is calculated by the results from the social force
model to achieve more robust prediction. Then, a one class support vector
machine (OCSVM) is applied in the measurement model of the PHD lter,
trained on human features, to mitigate noise from the environment and to
achieve better tracking performance.
The proposed improvements of the particle PHD lters are evaluated
with benchmark datasets such as the CAVIAR, PETS2009 and TUD datasets
and assessed with quantitative and global evaluation measures, and are compared
with state-of-the-art techniques to con rm the improvement of multiple
human tracking performance
Visual Tracking Using an Insect Vision Embedded Particle Filter
Particle filtering (PF) based object tracking algorithms have drawn great attention from lots of scholars. The core of PF is to predict the possible location of the target via the state transition model. One commonly adopted approach is resorting to prior motion cues under the smooth motion assumption, which performs well when the target moves with a relatively stable velocity. However, it would possibly fail if the target is undergoing abrupt motion. To address this problem, inspired by insect vision, we propose a simple yet effective visual tracking framework based on PF. Utilizing the neuronal computational model of the insect vision, we estimate the motion of the target in a novel way so as to refine the position state of propagated particles using more accurate transition mode. Furthermore, we design a novel sample optimization framework where local and global search strategies are jointly used. In addition, we propose a new method to monitor long duration severe occlusion and we could recover the target. Experiments on publicly available benchmark video sequences demonstrate that the proposed tracking algorithm outperforms the state-of-the art methods in challenging scenarios, especially for tracking target which is undergoing abrupt motion or fast movement.</jats:p
Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling
Stochastic sampling based trackers have shown good performance for abrupt
motion tracking so that they have gained popularity in recent years. However,
conventional methods tend to use a two-stage sampling paradigm, in which the
search space needs to be uniformly explored with an inefficient preliminary
sampling phase. In this paper, we propose a novel sampling-based method in the
Bayesian filtering framework to address the problem. Within the framework,
nearest neighbor field estimation is utilized to compute the importance
proposal probabilities, which guide the Markov chain search towards promising
regions and thus enhance the sampling efficiency; given the motion priors, a
smoothing stochastic sampling Monte Carlo algorithm is proposed to approximate
the posterior distribution through a smoothing weight-updating scheme.
Moreover, to track the abrupt and the smooth motions simultaneously, we develop
an abrupt-motion detection scheme which can discover the presence of abrupt
motions during online tracking. Extensive experiments on challenging image
sequences demonstrate the effectiveness and the robustness of our algorithm in
handling the abrupt motions.Comment: submitted to Elsevier Neurocomputin
The Neural Particle Filter
The robust estimation of dynamically changing features, such as the position
of prey, is one of the hallmarks of perception. On an abstract, algorithmic
level, nonlinear Bayesian filtering, i.e. the estimation of temporally changing
signals based on the history of observations, provides a mathematical framework
for dynamic perception in real time. Since the general, nonlinear filtering
problem is analytically intractable, particle filters are considered among the
most powerful approaches to approximating the solution numerically. Yet, these
algorithms prevalently rely on importance weights, and thus it remains an
unresolved question how the brain could implement such an inference strategy
with a neuronal population. Here, we propose the Neural Particle Filter (NPF),
a weight-less particle filter that can be interpreted as the neuronal dynamics
of a recurrently connected neural network that receives feed-forward input from
sensory neurons and represents the posterior probability distribution in terms
of samples. Specifically, this algorithm bridges the gap between the
computational task of online state estimation and an implementation that allows
networks of neurons in the brain to perform nonlinear Bayesian filtering. The
model captures not only the properties of temporal and multisensory integration
according to Bayesian statistics, but also allows online learning with a
maximum likelihood approach. With an example from multisensory integration, we
demonstrate that the numerical performance of the model is adequate to account
for both filtering and identification problems. Due to the weightless approach,
our algorithm alleviates the 'curse of dimensionality' and thus outperforms
conventional, weighted particle filters in higher dimensions for a limited
number of particles
Multimodal methods for blind source separation of audio sources
The enhancement of the performance of frequency domain convolutive
blind source separation (FDCBSS) techniques when applied to the
problem of separating audio sources recorded in a room environment
is the focus of this thesis. This challenging application is termed the
cocktail party problem and the ultimate aim would be to build a machine
which matches the ability of a human being to solve this task.
Human beings exploit both their eyes and their ears in solving this task
and hence they adopt a multimodal approach, i.e. they exploit both
audio and video modalities. New multimodal methods for blind source
separation of audio sources are therefore proposed in this work as a
step towards realizing such a machine.
The geometry of the room environment is initially exploited to improve
the separation performance of a FDCBSS algorithm. The positions
of the human speakers are monitored by video cameras and this
information is incorporated within the FDCBSS algorithm in the form
of constraints added to the underlying cross-power spectral density
matrix-based cost function which measures separation performance. [Continues.
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