Abstract: A method and apparatus for selecting a value or change in value of a measurement variable for an observation of an object, comprising: receiving models for the object defined in terms of an observation parameter and a measurement variable; selecting values of the measurement variable; for each model, determining a value of the observation parameter for each selected value; for each selected value, determining a value of an expected classification potential level using the determined values; and selecting a value of the measurement variable dependent upon the potential level values; wherein the potential level is an expected level of: the information or lack of information, and/or the certainty or uncertainty, with which the object could be classified if a measurement of the observation parameter were taken of the object at the respective value of the measurement variable.

Abstract: A method and apparatus for processing an observation of an object to be classified, the method comprising using a particle filter in which an observation of an object to be classified is used to weight particles on a plurality of models for the object. The method may further comprise performing at least one further iteration of using the particle filter to weight the particles on the plurality of models for the object using a further observation of the object to be classified. The method may further comprise providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model. Each of the respective particles may be divided into a respective plurality of sub-particles.

Abstract: An apparatus and method are disclosed for analysing a process. An exemplary method includes: generating a process template; and determining a probabilistic model specifying the process template. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, wherein observables may further be caused by background activities of the background node. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, observables may further be caused by background activities of the background node. The method can include measuring values of an observable corresponding to one of the observables nodes; and updating a probabilistic estimate of the process state using the measured values.

Abstract: Apparatus and method for processing a sequence of images of a scene, the method including: tracking a region of interest in the sequence of images (e.g. using a Self Adaptive Discriminant filter), selecting a particular image in the sequence, selecting a set of images from the sequence, the set of images including one or more images that precede the particular image in the sequence of images; and determining a value indicative of the level of change between the region of interest in the particular image and the regions of interest in the images in the set of images (e.g. using a Change Detection Process).

Abstract: Apparatus and method for processing a sequence of images of a scene, the method including: tracking a region of interest in the sequence of images (e.g. using a Self Adaptive Discriminant filter); selecting a particular image in the sequence; selecting a set of images from the sequence, the set having one or more images that precede the particular image in the sequence of images; for each pixel in the region of interest in the particular image, determining a value for a parameter; for each pixel in the region of interest of each image in the set of images, determining a value for the parameters; and comparing a function of the determined values for the region of interest in the particular image to a further function of the determined values for the regions of interest in the images in the set of images.

Abstract: Apparatus and method for processing a sequence of images of a scene, the method including: tracking a region of interest in the sequence of images (e.g. using a Self Adaptive Discriminant filter); selecting a particular image in the sequence; selecting a set of images from the sequence, the set having one or more images that precede the particular image in the sequence of images; for each pixel in the region of interest in the particular image, determining a value for a parameter; for each pixel in the region of interest of each image in the set of images, determining a value for the parameters; and comparing a function of the determined values for the region of interest in the particular image to a further function of the determined values for the regions of interest in the images in the set of images.

Abstract: Apparatus and method for processing a sequence of images of a scene, the method including: tracking a region of interest in the sequence of images (e.g. using a Self Adaptive Discriminant filter), selecting a particular image in the sequence, selecting a set of images from the sequence, the set of images including one or more images that precede the particular image in the sequence of images; and determining a value indicative of the level of change between the region of interest in the particular image and the regions of interest in the images in the set of images (e.g. using a Change Detection Process).

Abstract: A method for estimating a system state, in a decentralized network having a plurality of nodes, each node being confined for receiving and sending information, and for processing information. Each node being connected to neighboring nodes of the network. At each node the method includes: (i) maintaining a set of particles and associated weights, which represent an estimate of the system state, (ii) representing the estimated system state as a mixture of Gaussian distributions in a channel filter, and communicating the mixture to neighboring nodes, and (iii) a neighboring node receiving the mixture in a channel filter that contains a similar Gaussian representation of its own estimate of system state, and dividing the incoming mixture by the existing mixture, for updating the estimate of the system state that is maintained at the node.

Abstract: An apparatus and method are disclosed for analysing a process. An exemplary method includes: generating a process template; and determining a probabilistic model specifying the process template. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, wherein observables may further be caused by background activities of the background node. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, observables may further be caused by background activities of the background node. The method can include measuring values of an observable corresponding to one of the observables nodes; and updating a probabilistic estimate of the process state using the measured values.

Abstract: A method of generating an indication of a probability of a hypothesis being correct based on a set of observations includes obtaining data representing first and second sets of observations. Data representing a set of hypotheses at least partially derivable from the first and the second set of observations can also be obtained. Plural data associations can be generated between at least some data in the first and second sets to indicate a probability of at least some of the generated data associations being correct. The data representing the set of hypotheses and the indication of the probability of at least some of the generated data associations being correct can be used to generate an indication of a probability of at least one of the hypotheses represented by the data being correct.

Abstract: A method and apparatus for processing an observation of an object to be classified, the method comprising using a particle filter in which an observation of an object to be classified is used to weight particles on a plurality of models for the object. The method may further comprise performing at least one further iteration of using the particle filter to weight the particles on the plurality of models for the object using a further observation of the object to be classified. The method may further comprise providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model. Each of the respective particles may be divided into a respective plurality of sub-particles.

Abstract: A method and apparatus for selecting a value or change in value of a measurement variable for an observation of an object, comprising: receiving models for the object defined in terms of an observation parameter and a measurement variable; selecting values of the measurement variable; for each model, determining a value of the observation parameter for each selected value; for each selected value, determining a value of an expected classification potential level using the determined values; and selecting a value of the measurement variable dependent upon the potential level values; wherein the potential level is an expected level of: the information or lack of information, and/or the certainty or uncertainty, with which the object could be classified if a measurement of the observation parameter were taken of the object at the respective value of the measurement variable.

Abstract: A method and system are disclosed for tracking a target imaged in video footage. The target may, for example, be a person moving through a crowd The method comprises the steps of: identifying a target in a first frame; generating a population of sub-templates by sampling from a template area defined around the target position; and searching for instances of the sub-templates in a second frame so as to locate the target in the second frame. Sub-templates whose instances are not consistent with the new target position are removed from the population and replaced by newly sampled sub-templates. The method can then be repeated so as to find the target in further frames. It can be implemented in a system comprising video imaging means, such as a CCTV camera, and processing means operable to carry out the method.

Abstract: A method and system are disclosed for tracking a target imaged in video footage. The target may, for example, be a person moving through a crowd The method comprises the steps of: identifying a target in a first frame; generating a population of sub-templates by sampling from a template area defined around the target position; and searching for instances of the sub-templates in a second frame so as to locate the target in the second frame. Sub-templates whose instances are not consistent with the new target position are removed from the population and replaced by newly sampled sub-templates. The method can then be repeated so as to find the target in further frames. It can be implemented in a system comprising video imaging means, such as a CCTV camera, and processing means operable to carry out the method.

Abstract: A method for estimating a system state, for example an aircraft trajectory, in a decentralised network having a plurality of nodes, each node having means for receiving and sending information and means for processing information, and each node being connected to neighbouring nodes of the network, the method comprising, at each node: (i) maintaining a set of particles and associated weights, which represent an estimate of the system state, (ii) representing the estimated system state as a mixture of Gaussian distributions in a channel filter, and communicating the mixture to neighbouring nodes, and (iii) a neighbouring node receiving the mixture in a channel filter that contains a similar Gaussian representation of its own estimate of system state, and dividing the incoming mixture by the existing mixture, for updating the estimate of the system state that is maintained at the node.