The following topics are dealt with: targettracking; datafusion; particle filtering; multisensor; MAP estimation; video tracking; random matrix; motion estimation; MANET; mobile robot; Kalman filter; object detectio...
The following topics are dealt with: targettracking; datafusion; particle filtering; multisensor; MAP estimation; video tracking; random matrix; motion estimation; MANET; mobile robot; Kalman filter; object detection and laser range scanner.
QinetiQ and Southampton University are engaged in a programme on fusion of novel biometrics for real-world secure environments. The main objective is to develop a camera system for measuring and fusing two key biometr...
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Posterior densities in nonlinear tracking problems can successfully be constructed using particle filtering. The mean of the density is a popular point estimate. However, especially in multi-modal densities it does no...
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ISBN:
(纸本)9780863419102
Posterior densities in nonlinear tracking problems can successfully be constructed using particle filtering. The mean of the density is a popular point estimate. However, especially in multi-modal densities it does not always represent a reasonable estimate. In multi-targettracking the mean can produce a large bias when there is uncertainty about the labelling of the tracks, also referred to as the mixed labelling problem. The particle based Maximum A Posteriori (MAP) point estimator that has been recently developed is applied to this problem. It is shown by means of simulation that it provides a large improvement over the mean estimate.
Scattering of signals from commercial television or radio channels from air and surface targets can be detected by one or more passive receivers. The received signals can be processed to give bistatic range and range ...
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ISBN:
(纸本)9780863419102
Scattering of signals from commercial television or radio channels from air and surface targets can be detected by one or more passive receivers. The received signals can be processed to give bistatic range and range rate measurements on targets. This paper considers trackingalgorithms to convert the measurements to target tracks using data from a demonstration receiver, with emphasis on the problem of track initiation for low SNR targets where there are many false detections. It is shown that it is possible to track targets in this type of environment. Further development and tuning is required to minimise the number of false tracks initiated.
We present an innovative optical flow based algorithm that uses a memory of both target appearance and motion in order to simultaneously segment and track extended targets through complex scenes. A particularly attrac...
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ISBN:
(纸本)9780863419102
We present an innovative optical flow based algorithm that uses a memory of both target appearance and motion in order to simultaneously segment and track extended targets through complex scenes. A particularly attractive feature of this approach is that is assumes little prior knowledge of the scene content (background, clutter etc) and can cope with a variety of target types and numbers. The algorithm will be demonstrated on synthetic and real-world, visual-band imagery.
Conditional independence is perhaps the most pervasive of all assumptions used in datafusion and estimation algorithms. If a noise corrupted observation is conditionally independent of the system state, Bayes Rule ca...
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ISBN:
(纸本)9780863419102
Conditional independence is perhaps the most pervasive of all assumptions used in datafusion and estimation algorithms. If a noise corrupted observation is conditionally independent of the system state, Bayes Rule can be used in an optimal, recursive manner. However, this assumption rarely holds true in practice. One cause of dependency is modelling errors: any process or observation model is an approximation and incorrectly describes the true response of the system. A second cause is simplification. Even if the models are precisely known, the full Bayesian computations could be prohibitively expensive and some approximation must be used.
Artificial neural-networks have been widely applied in various aspects of particle-tracking velocimetry. This paper presents an overview of the different applications and gives an insight into how this technology can ...
Artificial neural-networks have been widely applied in various aspects of particle-tracking velocimetry. This paper presents an overview of the different applications and gives an insight into how this technology can be applied to fuel-flow monitoring. The paper presents a method of flow-field estimation based on particle-tracking velocimetry, without the need to solve the correspondence problem. We also present a method of defeating the obscuration problem found in many optical velocimetry schemes.
In this paper, we describe a video tracking application using the dual-tree polar matching algorithm. The models are specified in a probabilistic setting, and a particle filter is used to perform the sequential infere...
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ISBN:
(纸本)9780863419102
In this paper, we describe a video tracking application using the dual-tree polar matching algorithm. The models are specified in a probabilistic setting, and a particle filter is used to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to track a simulated video moving target in an urban environment with complete and partial occlusions.
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