2,704 research outputs found
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
In a typical multitarget tracking (MTT) scenario, the sensor state is either
assumed known, or tracking is performed in the sensor's (relative) coordinate
frame. This assumption does not hold when the sensor, e.g., an automotive
radar, is mounted on a vehicle, and the target state should be represented in a
global (absolute) coordinate frame. Then it is important to consider the
uncertain location of the vehicle on which the sensor is mounted for MTT. In
this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT
filter, which jointly tracks the uncertain vehicle state and target states.
Measurements collected by different sensors mounted on multiple vehicles with
varying location uncertainty are incorporated sequentially based on the arrival
of new sensor measurements. In doing so, targets observed from a sensor mounted
on a well-localized vehicle reduce the state uncertainty of other poorly
localized vehicles, provided that a common non-empty subset of targets is
observed. A low complexity filter is obtained by approximations of the joint
sensor-feature state density minimizing the Kullback-Leibler divergence (KLD).
Results from synthetic as well as experimental measurement data, collected in a
vehicle driving scenario, demonstrate the performance benefits of joint
vehicle-target state tracking.Comment: 13 pages, 7 figure
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
Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior
for multiple extended object filtering. A Poisson point process is used to
describe the existence of yet undetected targets, while a multi-Bernoulli
mixture describes the distribution of the targets that have been detected. The
prediction and update equations are presented for the standard transition
density and measurement likelihood. Both the prediction and the update preserve
the PMBM form of the density, and in this sense the PMBM density is a conjugate
prior. However, the unknown data associations lead to an intractably large
number of terms in the PMBM density, and approximations are necessary for
tractability. A gamma Gaussian inverse Wishart implementation is presented,
along with methods to handle the data association problem. A simulation study
shows that the extended target PMBM filter performs well in comparison to the
extended target d-GLMB and LMB filters. An experiment with Lidar data
illustrates the benefit of tracking both detected and undetected targets
Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets
AbstractThe probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches
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