Abstract: A method of detecting a change in a scene between images that capture a scene at different times, the images comprising reference images and a query image, the method includes reconstructing the query image using a reconstruction model, the reconstruction model being based on the reference images and detecting a change in the scene by comparing the query image with the reconstructed query image.

Abstract: A method of detecting a change in a scene between images that capture a scene at different times, the images comprising reference images and a query image, the method includes reconstructing the query image using a reconstruction model, the reconstruction model being based on the reference images and detecting a change in the scene by comparing the query image with the reconstructed query image.

Abstract: A method of matching a first person in a first image to a person in a second image. At least one companion in the first image is determined and being different to the first person. A contextual confidence for the first person, the companion and each of a plurality of people in the second image is determined, the contextual confidence being a measure of prediction accuracy of a match to the first person. An appearance score between each person in the first image and each of the plurality of people in the second image, the appearance score measuring similarity of appearance. The method selects from the plurality of people in the second image, a match for the first person and, based on the match for the first person, a match for the companion, each of the matches being determined according to the contextual confidence and appearance score.

Abstract: A system and method for determining an object in a distorted image. The method comprises detecting a location of a first part of the object on the distorted image; determining a corresponding location of the first part of the object on an undistorted image plane corresponding to the distorted image based on a plurality of calibration parameters; predicting a location of a second part of the object on the undistorted image plane based on the determined location of the first part on the undistorted image plane; determining a corresponding location of the second part of the object on the distorted image based on the predicted location of the second part of the object on the undistorted image plane and the calibration parameters; and determining the object based on the detected location of the first part of the object and the determined location of the second part in the distorted image.

Abstract: A method of determining a vertical medial axis of an object in an input image, is disclosed. A measure of horizontal symmetry is determined for each pixel of the image. First and second vertical bounds within the image are determined. A continuous path between the first and second vertical bounds is determined based on the horizontal symmetry measure, the continuous path representing the vertical medial axis of the object.

Abstract: A method of identifying, with a camera, an object in an image of a scene, by determining the distinctiveness of each of a number of attributes of an object of interest, independent of the camera viewpoint, determining the detectability of each of the attributes based on the relative orientation of a candidate object in the image of the scene, determining a camera setting for viewing the candidate object based on the distinctiveness of an attribute, so as to increase the detectability of the attribute, and capturing an image of the candidate object with the camera setting to determine the confidence that the candidate object is the object of interest.

Abstract: A method of determining a pose for an object. A plurality of images capturing the object at different viewpoints are received. The viewpoints are related by an angular distance with respect to the object. A feature vector is extracted for the object from each of the received images. Each extracted feature vector is compared with feature vectors from a database to determine a plurality of candidate poses. A pose of the object is determined by comparing candidate poses associated with different ones of the viewpoints, using the angular distance between the viewpoints with respect to the object.

Abstract: Methods, systems, and computer readable media are described for controlling a camera to perform a selected task from a set of tasks is provided. The method comprises determining a viewing condition of the camera to perform each task from the set of tasks, and determining a posterior probability of task success for each task from the set of tasks based on the determined viewing conditions and a prior probability of task success for each task. The method also includes determining a change in rate of information gain for task success for each task from the set of tasks based on the posterior probability, selecting the task to be performed based on the change in rate of information gain, and controlling the camera to perform the selected task.

Abstract: A system and method for determining an object in a distorted image. The method comprises detecting a location of a first part of the object on the distorted image; determining a corresponding location of the first part of the object on an undistorted image plane corresponding to the distorted image based on a plurality of calibration parameters; predicting a location of a second part of the object on the undistorted image plane based on the determined location of the first part on the undistorted image plane; determining a corresponding location of the second part of the object on the distorted image based on the predicted location of the second part of the object on the undistorted image plane and the calibration parameters; and determining the object based on the detected location of the first part of the object and the determined location of the second part in the distorted image.

Abstract: A method of matching a person in captured images comprises determining first feature vectors from a first image sequence of person(s), and determining second feature vectors from a second image sequence of person(s). The first and second feature vectors are determined based on properties of pixels located in the first and second image sequences respectively. The method further comprises, for a first feature vector corresponding to a first person in the first image sequence, determining a reference distance to one of the second feature vectors corresponding to a reference person in the second image sequence; determining a distance metric by constraining a distance between the first feature vector and a feature vector corresponding to the first person in the second image sequence, according to the determined reference distance; and matching a pair of images of the person in the captured images based on the distance metric.

Abstract: A method and system associated with a camera view of a moving-object in a scene. The method comprises detecting and tracking the moving object over multiple video frames, estimating an orientation of the moving object in each of the video frames, and constructing a cost map from the estimated orientations over the multiple video frames for finding a minimum cost path over the cost map. The Method also comprises determining regularized orientation estimates of the moving-object from the minimum cost path, and locating the vanishing point of the camera view based on an axis of the moving-object from the minimum cost path, the axis formed by using the regularized orientation estimates.

Abstract: A method of identifying, with a camera, an object in an image of a scene, by determining the distinctiveness of each of a number of attributes of an object of interest, independent of the camera viewpoint, determining the detectability of each of the attributes based on the relative orientation of a candidate object in the image of the scene, determining a camera setting for viewing the candidate object based on the distinctiveness of an attribute, so as to increase the detectability of the attribute, and capturing an image of the candidate object with the camera setting to determine the confidence that the candidate object is the object of interest.

Abstract: A method and system associated with a camera view of a moving-object in a scene. The method comprises detecting and tracking the moving object over multiple video frames, estimating an orientation of the moving object in each of the video frames, and constructing a cost map from the estimated orientations over the multiple video frames for finding a minimum cost path over the cost map. The Method also comprises determining regularised orientation estimates of the moving-object from the minimum cost path, and locating the vanishing point of the camera view based on an axis of the moving-object from the minimum cost path, the axis formed by using the regularised orientation estimates.

Abstract: A method of identifying an object in an image is disclosed. At least one feature map for each of a plurality of cells in the image is determined. A self-dissimilarity between a first feature map associated with a first one of said cells and a second feature map associated with a second cell, is determined. The self-dissimilarity is determined by determining a sum over thresholds of a difference in area between the first feature map and the second feature map. An appearance signature for the object is formed based on the determined self-dissimilarity. A distance between the appearance signature of the object in the image and appearance signatures of each of a plurality of further objects is determined. The object in the image is identified based on the determined distances.

Abstract: A method of identifying, with a camera, an object in an image of a scene, by determining the distinctiveness of each of a number of attributes of an object of interest, independent of the camera viewpoint, determining the detectability of each of the attributes based on the relative orientation of a candidate object in the image of the scene, determining a camera setting for viewing the candidate object based on the distinctiveness of an attribute, so as to increase the detectability of the attribute, and capturing an image of the candidate object with the camera setting to determine the confidence that the candidate object is the object of interest.

Abstract: A method of determining a property of an image (176) captured by a camera (127). Vanishing points (320, 330, 340) are in the image (176). Each pixel of the image (176) is associated with one or more of the vanishing points (320, 330, 340) based on an orientation of the image gradient at the pixel. The image is partitioned into a set of regions associated with a pair of the determined vanishing points based on the vanishing point associations for pixels in the image (176). Boundaries of the regions are aligned with the associated vanishing points. For at least one of the plurality of regions, a confidence value is determined for the property of the image based on pixels in the region. The property of the image is determined for one or more pixels within the image (176) based on the confidence value.

Abstract: Methods, systems, and computer readable media are described for controlling a camera to perform a selected task from a set of tasks is provided. The method comprises determining a viewing condition of the camera to perform each task from the set of tasks, and determining a posterior probability of task success for each task from the set of tasks based on the determined viewing conditions and a prior probability of task success for each task. The method also includes determining a change in rate of information gain for task success for each task from the set of tasks based on the posterior probability, selecting the task to be performed based on the change in rate of information gain, and controlling the camera to perform the selected task.

Abstract: A method of identifying an object in an image is disclosed. At least one feature map for each of a plurality of cells in the image is determined. A self-dissimilarity between a first feature map associated with a first one of said cells and a second feature map associated with a second cell, is determined. The self-dissimilarity is determined by determining a sum over thresholds of a difference in area between the first feature map and the second feature map. An appearance signature for the object is formed based on the determined self-dissimilarity. A distance between the appearance signature of the object in the image and appearance signatures of each of a plurality of further objects is determined. The object in the image is identified based on the determined distances.

Abstract: A method of determining a vertical medial axis of an object in an input image, is disclosed. A measure of horizontal symmetry is determined for each pixel of the image. First and second vertical bounds within the image are determined. A continuous path between the first and second vertical bounds is determined based on the horizontal symmetry measure, the continuous path representing the vertical medial axis of the object.

Abstract: A method of detecting a supporting surface region in an image captured by a camera is disclosed. An object in the image is detected. One or more regions of the image in which a lower part of the detected object exists are determined. A degree of confidence for each of the regions is determined. The degree of confidence indicates likelihood of a corresponding region being a supporting surface region. One or more of the regions are selected based on each corresponding degree of confidence. Similarity of other regions in the image to at least one of the selected regions is determined. The supporting surface region is detected based on the determined similarity.