Abstract: A system for generating a labelled dataset is provided. The system comprises processor configured to: receive first data wherein the first data comprises one or more frames and wherein the first data comprises data defining an object of interest within a predetermined area; receive second data wherein the second data is associated with the object of interest within the predetermined area; analyse the one or more frames of the first data to identify, based on the second data, the object of interest present in the first data; label the one or more frames of the first data based on the analysis to generate a labelled dataset; and output the labelled dataset. Also provided is a method for generating a labelled dataset, a system for training a machine learning model, and a detection system for detecting one or more objects of interest.

Abstract: There is provided an image processing system and method for identifying a user. The system comprises a processor configured to identify a first user in an image, determine a plurality of characteristic vectors associated with the first user, compare the characteristic vectors associated with the first user with a plurality of predetermined characteristic vectors associated with a plurality of users including the first user, and identify the first user based on the comparison.

Abstract: Systems and methods for detecting patterns in data from a time-series and for detecting outliers in network data in an unsupervised manner are provided. In one implementation, a method includes the steps of obtaining network data from a network to be monitored and creating a window from the obtained network data. The method also includes the step of detecting outliers of the obtained data with respect to the window using an unsupervised deep learning process (e.g., using a Generalized Adversarial Network (GAN) learning technique and/or a Bidirectional GAN (BiGAN) learning technique) for enabling the learning of a data distribution. The unsupervised process, for example, does not require manual intervention.

Abstract: Methods for identifying and tracking an article are disclosed. During a journey, an article follows a path between an origin and a destination. An image of the article is captured during the journey and a first characteristic vector determined from the image of the article. The first characteristic vector is compared with a set of predetermined characteristic vectors and, based on the comparison, the first article is either associated with an identifier associated with a corresponding one of the predetermined characteristic vectors, or is associated with a new identifier.

Abstract: A system for generating a labelled dataset is provided. The system comprises processor configured to: receive first data wherein the first data comprises one or more frames and wherein the first data comprises data defining an object of interest within a predetermined area; receive second data wherein the second data is associated with the object of interest within the predetermined area; analyse the one or more frames of the first data to identify, based on the second data, the object of interest present in the first data; label the one or more frames of the first data based on the analysis to generate a labelled dataset; and output the labelled dataset. Also provided is a method for generating a labelled dataset, a system for training a machine learning model, and a detection system for detecting one or more objects of interest.

Abstract: Methods for identifying and tracking an article are disclosed. During a journey, an article follows a path between an origin and a destination. An image of the article is captured during the journey and a first characteristic vector determined from the image of the article. The first characteristic vector is compared with a set of predetermined characteristic vectors and, based on the comparison, the first article is either associated with an identifier associated with a corresponding one of the predetermined characteristic vectors, or is associated with a new identifier.

Abstract: Systems and methods for detecting patterns in data from a time-series are provided. In one implementation, a method for pattern detection includes obtaining data in a time-series and creating one-dimensional or multi-dimensional windows from the time-series data. The one-dimensional or multi-dimensional windows are created either independently or jointly with the time-series. The method also includes training a deep neural network with the one-dimensional or multi-dimensional windows utilizing historical and/or simulated data to provide a neural network model. Also, the method includes processing ongoing data with the neural network model to detect one or more patterns of a particular category in the ongoing data, and localizing the one or more patterns in time.

Abstract: There is provided an image processing system and method for identifying a user. The system comprises a processor configured to identify a first user in an image, determine a plurality of characteristic vectors associated with the first user, compare the characteristic vectors associated with the first user with a plurality of predetermined characteristic vectors associated with a plurality of users including the first user, and identify the first user based on the comparison.

Abstract: Systems and methods for detecting patterns in data from a time-series and for detecting outliers in network data in an unsupervised manner are provided. In one implementation, a method includes the steps of obtaining network data from a network to be monitored and creating a window from the obtained network data. The method also includes the step of detecting outliers of the obtained data with respect to the window using an unsupervised deep learning process (e.g., using a Generalized Adversarial Network (GAN) learning technique and/or a Bidirectional GAN (BiGAN) learning technique) for enabling the learning of a data distribution. The unsupervised process, for example, does not require manual intervention.

Abstract: Systems and methods for detecting patterns in data from a time-series and for detecting outliers in network data in an unsupervised manner are provided. In one implementation, a method includes the steps of obtaining network data from a network to be monitored and creating a window from the obtained network data. The method also includes the step of detecting outliers of the obtained data with respect to the window using an unsupervised deep learning process (e.g., using a Generalized Adversarial Network (GAN) learning technique and/or a Bidirectional GAN (BiGAN) learning technique) for enabling the learning of a data distribution. The unsupervised process, for example, does not require manual intervention.

Abstract: Systems and methods for detecting patterns in data from a time-series are provided. In one implementation, a method for pattern detection includes obtaining data in a time-series and creating one-dimensional or multi-dimensional windows from the time-series data. The one-dimensional or multi-dimensional windows are created either independently or jointly with the time-series. The method also includes training a deep neural network with the one-dimensional or multi-dimensional windows utilizing historical and/or simulated data to provide a neural network model. Also, the method includes processing ongoing data with the neural network model to detect one or more patterns of a particular category in the ongoing data, and localizing the one or more patterns in time.