Abstract: A method for generating an optimised domain-generalisable model for re-identification of a target in a set of candidate images. The method optimises a local feature embedding model for domain-specific feature representation at each client of a plurality of clients, then receives, at a central server, information on changes to the local feature embedding model at each respective client resulting from the optimising step, and then updates a global feature embedding model based on the changes to the local feature embedding model. The method further receives, at each client from the central server, information representative of the updates to the global feature embedding model, then maps, at each client, on to the respective local feature embedding model at least a portion of the received updates, and subsequently updates, at each client, the respective local feature embedding model based on the mapped updates.

Abstract: Method and system for building a machine learning model for finding visual targets from text queries, the method comprising the steps of receiving a set of training data comprising text attribute labelled images, wherein each image has more than one text attribute label. Receiving a first vector space comprising a mapping of words, the mapping defining relationships between words. Generating a visual feature vector space by grouping images of the set of training data having similar attribute labels. Mapping each attribute label within the training data set on to the first vector space to form a second vector space. Fusing the visual feature vector space and the second vector space to form a third vector space. Generating a similarity matching model from the third vector space.

Abstract: Method for optimising a reinforcement learning model comprising the steps of receiving a labelled data set. Receiving an unlabelled data set. Generating model parameters to form an initial reinforcement learning model using the labelled data set as a training data set. Finding a plurality of matches for one or more target within the unlabelled data set using the initial reinforcement learning model. Ranking the plurality of matches. Presenting a subset of the ranked matches and corresponding one or more target, wherein the subset of ranked matches includes the highest ranked matches. Receiving a signal indicating that one or more presented match of the highest ranked matches is an incorrect match. Adding information describing the indicated incorrect one or more match and corresponding target to the labelled data set to form a new training data set. Updating the model parameters of the initial reinforcement learning model to form an updated reinforcement learning model using the new training data set.

Abstract: A computer implemented method and system for training a machine to identify a target within video data, the method comprising the steps of providing a training data set including identified labelled targets within video data having the same target within different video views. Generating, using a learning model, a bounding box action policy for determining required adjustments to a bounding box around a target in the video data by: generating a bounding box around a labelled target within a first view of the video data. Converting the target bounded by the bounding box to a quantitative representation. Determining a matching level between the quantitative representation and a quantitative representation of a further labelled target within the video data from a second view different to the first view. Looping the following steps one or more times, the looped steps comprising: using the bounding box action policy to determine an action to change the bounding box.

Abstract: A computer implemented method and system for training a machine to identify a target within video data, the method comprising the steps of providing a training data set including identified labelled targets within video data having the same target within different video views. Generating, using a learning model, a bounding box action policy for determining required adjustments to a bounding box around a target in the video data by: generating a bounding box around a labelled target within a first view of the video data. Converting the target bounded by the bounding box to a quantitative representation. Determining a matching level between the quantitative representation and a quantitative representation of a further labelled target within the video data from a second view different to the first view. Looping the following steps one or more times, the looped steps comprising: using the bounding box action policy to determine an action to change the bounding box.

Abstract: Method and processor for searching for a target within video data comprising the steps of receiving a target selected from within video data. Identifying a current selection of target matches for the selected target within further video data. Ranking the current selection of target matches. Receiving a signal confirming or rejecting one or more of the ranked target matches. Identifying a further selection of target matches for the confirmed target matches from the further video data. Indicating portions of the further video data containing the further selection of target matches.

Abstract: A method and system for analyzing a queue comprising: obtaining a first image acquired at a first time of a first position within a queue; obtaining a second image acquired at a second time of a second position within the queue; detecting a queue member within the first image; detecting a queue member with the second image; determining that the queue member detected within the second image is the same as the queue member detected within the first image; and determining a trajectory of the queue member within the queue based on a difference between the first time and the second time.

Abstract: Method and processor for searching for a target within video data comprising the steps of receiving a target selected from within video data. Identifying a current selection of target matches for the selected target within further video data. Ranking the current selection of target matches. Receiving a signal confirming or rejecting one or more of the ranked target matches. Identifying a further selection of target matches for the confirmed target matches from the further video data. Indicating portions of the further video data containing the further selection of target matches.

Abstract: A method and system for analysing a queue comprising: obtaining a first image acquired at a first time of a first position within a queue; obtaining a second image acquired at a second time of a second position within the queue; detecting a queue member within the first image; detecting a queue member with the second image; determining that the queue member detected within the second image is the same as the queue member detected within the first image; and determining a trajectory of the queue member within the queue based on a difference between the first time and the second time.