Abstract: A system and method are provided for transacting data on a network. The system includes a data submission interface, the data submission interface configured to enable one or more data generators to register data with the network, the data being stored in a location accessible to data consumers accessing the network. The system also includes a data consumer interface to enable one or more data consumers to find data on the network and transact with the one or more data generators to obtain access to and/or ownership of data submitted to the network. The system also includes a blockchain network for storing at least a ledger associated with data registered with the network via the data submission interface to provide an immutable assignment of ownership rights of the data to the respective one or more data generators, and to provide an immutable record of data transactions.

Abstract: A system and method are provided for transacting data on a network. The system includes a data submission interface, the data submission interface configured to enable one or more data generators to register data with the network, the data being stored in a location accessible to data consumers accessing the network. The system also includes a data consumer interface to enable one or more data consumers to find data on the network and transact with the one or more data generators to obtain access to and/or ownership of data submitted to the network. The system also includes a blockchain network for storing at least a ledger associated with data registered with the network via the data submission interface to provide an immutable assignment of ownership rights of the data to the respective one or more data generators, and to provide an immutable record of data transactions.

Abstract: To address problems that video imaging systems and platforms face when analyzing image and video content for detection and feature extraction, a solution is provided in which accumulating significant amounts of data suitable for training and learning analytics is leveraged to improve over time, the classifiers used to perform the detection and feature extraction, by employing a larger search space and generate additional and more complex classifiers through distributed processing. A distributed learning platform is therefore provided, which is configured for operating on large scale data, in a true big data paradigm. The learning platform is operable to empirically estimate a set of optimal feature vectors and a set of discriminant functions using a parallelizable learning algorithm. A method of adding new data into a database utilized by such a learning platform is also provided.

Abstract: A system and method are provided for performing saliency detection on an image or video. The method includes training and creating deep features using deep neural networks, such that an input image is transformed into a plurality of regions, which minimizes intra-class variance, and maximizes inter-class variance, according to one or more active contour energy constraints. The method also includes providing and output associated with the deep features.

Abstract: A system and method are provided for compressing video data, the method comprising: obtaining video data; extracting at least one object of interest from the video data using at least one classifier associated with at least one feature detectable in the video data; and preparing compressed video data comprising at least one object of interest extracted from the video data.

Abstract: A method includes acquiring an image in the vehicle after determining that the vehicle is in or approaching an enforced area, and enabling the vehicle occupancy to be determined by wirelessly sending the image or a value indicative of the vehicle occupancy to an occupancy determining system. Determining vehicle occupancy can include receiving a vehicle identifier obtained by a vehicle detection camera at an enforced area; using the vehicle identifier to determine an associated user; sending a query to an occupancy app residing on a device for the associated user, the device being located in a vehicle corresponding to the vehicle identifier and configured to obtain in-vehicle images; receiving, in response to the query, an image or a value indicative of the vehicle occupancy to determine the vehicle's occupancy; and sending an occupancy event to an enforcement agency associated with the enforced area.

Abstract: A system and method are provided for mapping spatial and temporal measurements of motion constrained objects in a scene, e.g., vehicles. The method comprises determining a location parameter, and/or other interesting properties, for each of a plurality of objects at a plurality of points over time; generating a vector field over location and time using the location parameters, to specify the motion of each object over time; deriving measurements for each object using velocities from the vector field for that object, as the object moves through the scene over time; and outputting both individual and aggregate object and object property flow for the plurality of objects. Combining the generated map with a network graph and/or network model can provide network flow insights useful for historic event analysis, network flow monitoring, and planning purposes.

Abstract: A system and method are provided for performing saliency detection on an image or video. The method includes training and creating deep features using deep neural networks, such that an input image is transformed into a plurality of regions, which minimizes intra-class variance, and maximizes inter-class variance, according to one or more active contour energy constraints. The method also includes providing and output associated with the deep features.

Abstract: To address problems that video imaging systems and platforms face when analyzing image and video content for detection and feature extraction, a solution is provided in which accumulating significant amounts of data suitable for training and learning analytics is leveraged to improve over time, the classifiers used to perform the detection and feature extraction, by employing a larger search space and generate additional and more complex classifiers through distributed processing. A distributed learning platform is therefore provided, which is configured for operating on large scale data, in a true big data paradigm. The learning platform is operable to empirically estimate a set of optimal feature vectors and a set of discriminant functions using a parallelizable learning algorithm. A method of adding new data into a database utilized by such a learning platform is also provided.

Abstract: A system and method are provided for mapping spatial and temporal measurements of motion constrained objects in a scene, e.g., vehicles. The method comprises determining a location parameter, and/or other interesting properties, for each of a plurality of objects at a plurality of points over time; generating a vector field over location and time using the location parameters, to specify the motion of each object over time; deriving measurements for each object using velocities from the vector field for that object, as the object moves through the scene over time; and outputting both individual and aggregate object and object property flow for the plurality of objects. Combining the generated map with a network graph and/or network model can provide network flow insights useful for historic event analysis, network flow monitoring, and planning purposes.

Abstract: A system and method are provided for compressing video data, the method comprising: obtaining video data; extracting at least one object of interest from the video data using at least one classifier associated with at least one feature detectable in the video data; and preparing compressed video data comprising at least one object of interest extracted from the video data.