Abstract: Examples relate to an evaluation device for re-identification and to a corresponding method, system and computer program. The evaluation device comprises processing circuitry being configured to obtain a plurality of transformed re-identification codes, each transformed re-identification code being associated with a timestamp and location information. Each transformed re-identification code is based on a similarity-preserving transformation of a re-identification code that represents at least a portion of a sample of media data, the media data originating from two or more different sources located in two or more different locations. The processing circuitry is configured to match transformed re-identification codes among the plurality of transformed re-identification codes using a similarity metric to generate one or more tuples of transformed re-identification codes that are similar according to the similarity metric.

Abstract: Examples relate to an evaluation device for re-identification and to a corresponding method, system and computer program. The evaluation device comprises processing circuitry being configured to obtain a plurality of transformed re-identification codes, each transformed re-identification code being associated with a timestamp and location information. Each transformed re-identification code is based on a similarity-preserving transformation of a re-identification code that represents at least a portion of a sample of media data, the media data originating from two or more different sources located in two or more different locations. The processing circuitry is configured to match transformed re-identification codes among the plurality of transformed re-identification codes using a similarity metric to generate one or more tuples of transformed re-identification codes that are similar according to the similarity metric.

Abstract: Examples relate to a concept for generating training data and training a machine-learning model for use in re-identification. A computer system for generating training data for training a machine-learning model for use in re-identification comprising processing circuitry configured to obtain media data, the media data comprising a plurality of samples representing a person, an animal or an object. The processing circuitry is configured to process the media data to identify tuples of samples that represent the same person, animal or object. The processing circuitry is configured to generate the training data based on the identified tuples of samples that represent the same person, animal or object.

Abstract: Examples relate to an apparatus, a method and a computer program for detecting an anomaly in input data, to a camera device and a system comprising such an apparatus, and to a method and computer program for training a sequence of machine-learning models for use in anomaly detection. The apparatus for detecting an anomaly in input data is configured to process the input data using a sequence of machine-learning models. The sequence of machine-learning models comprising a first machine-learning model configured to pre-process the input data to provide pre-processed input data and a second machine-learning model configured to process the pre-processed input data to provide output data. The first machine-learning model is trained to transform the input data such, that the pre-processed input data comprises a plurality of sub-components being statistically independent with a known probability distribution. The second machine-learning model is an auto-encoder.

Abstract: Examples relate to a concept for generating training data and training a machine-learning model for use in re-identification. A computer system for generating training data for training a machine-learning model for use in re-identification comprising processing circuitry configured to obtain media data, the media data comprising a plurality of samples representing a person, an animal or an object. The processing circuitry is configured to process the media data to identify tuples of samples that represent the same person, animal or object. The processing circuitry is configured to generate the training data based on the identified tuples of samples that represent the same person, animal or object.

Abstract: Examples relate to an evaluation device for re-identification and to a corresponding method, system and computer program. The evaluation device comprises processing circuitry being configured to obtain a plurality of transformed re-identification codes, each transformed re-identification code being associated with a timestamp and location information. Each transformed re-identification code is based on a similarity-preserving transformation of a re-identification code that represents at least a portion of a sample of media data, the media data originating from two or more different sources located in two or more different locations. The processing circuitry is configured to match transformed re-identification codes among the plurality of transformed re-identification codes using a similarity metric to generate one or more tuples of transformed re-identification codes that are similar according to the similarity metric.