25 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
Matrix of Polynomials Model based Polynomial Dictionary Learning Method for Acoustic Impulse Response Modeling
We study the problem of dictionary learning for signals that can be
represented as polynomials or polynomial matrices, such as convolutive signals
with time delays or acoustic impulse responses. Recently, we developed a method
for polynomial dictionary learning based on the fact that a polynomial matrix
can be expressed as a polynomial with matrix coefficients, where the
coefficient of the polynomial at each time lag is a scalar matrix. However, a
polynomial matrix can be also equally represented as a matrix with polynomial
elements. In this paper, we develop an alternative method for learning a
polynomial dictionary and a sparse representation method for polynomial signal
reconstruction based on this model. The proposed methods can be used directly
to operate on the polynomial matrix without having to access its coefficients
matrices. We demonstrate the performance of the proposed method for acoustic
impulse response modeling.Comment: 5 pages, 2 figure
Low-dimensional Denoising Embedding Transformer for ECG Classification
The transformer based model (e.g., FusingTF) has been employed recently for
Electrocardiogram (ECG) signal classification. However, the high-dimensional
embedding obtained via 1-D convolution and positional encoding can lead to the
loss of the signal's own temporal information and a large amount of training
parameters. In this paper, we propose a new method for ECG classification,
called low-dimensional denoising embedding transformer (LDTF), which contains
two components, i.e., low-dimensional denoising embedding (LDE) and transformer
learning. In the LDE component, a low-dimensional representation of the signal
is obtained in the time-frequency domain while preserving its own temporal
information. And with the low dimensional embedding, the transformer learning
is then used to obtain a deeper and narrower structure with fewer training
parameters than that of the FusingTF. Experiments conducted on the MIT-BIH
dataset demonstrates the effectiveness and the superior performance of our
proposed method, as compared with state-of-the-art methods.Comment: To appear at ICASSP 202
An unsupervised acoustic fall detection system using source separation for sound interference suppression
We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person’s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove
the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods
Kalman-gain aided particle PHD filter for multi-target tracking
We propose an efficient SMC-PHD filter which employs the Kalman-gain approach during weight update to correct predicted particle states by minimizing the mean square error (MSE) between the estimated measurement and the actual measurement received at a given time in order to arrive at a more accurate posterior. This technique identifies and selects those particles belonging to a particular target from a given PHD for state correction during weight computation. Besides the improved tracking accuracy, fewer particles are required in the proposed approach. Simulation results confirm the improved tracking performance when evaluated with different measures
An unsupervised acoustic fall detection system using source separation for sound interference suppression
We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person׳s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods
Blood-coated sensor for high-throughput ptychographic cytometry on a Blu-ray disc
Blu-ray drive is an engineering masterpiece that integrates disc rotation,
pickup head translation, and three lasers in a compact and portable format.
Here we integrate a blood-coated image sensor with a modified Blu-ray drive for
high-throughput cytometric analysis of various bio-specimens. In this device,
samples are mounted on the rotating Blu-ray disc and illuminated by the
built-in lasers from the pickup head. The resulting coherent diffraction
patterns are then recorded by the blood-coated image sensor. The rich spatial
features of the blood-cell monolayer help down-modulate the object information
for sensor detection, thus forming a high-resolution computational bio-lens
with a theoretically unlimited field of view. With the acquired data, we
develop a lensless coherent diffraction imaging modality termed rotational
ptychography for image reconstruction. We show that our device can resolve the
435 nm linewidth on the resolution target and has a field of view only limited
by the size of the Blu-ray disc. To demonstrate its applications, we perform
high-throughput urinalysis by locating disease-related calcium oxalate crystals
over the entire microscope slide. We also quantify different types of cells on
a blood smear with an acquisition speed of ~10,000 cells per second. For in
vitro experiment, we monitor live bacterial cultures over the entire Petri dish
with single-cell resolution. Using biological cells as a computational lens
could enable new intriguing imaging devices for point-of-care diagnostics.
Modifying a Blu-ray drive with the blood-coated sensor further allows the
spread of high-throughput optical microscopy from well-equipped laboratories to
citizen scientists worldwide