Abstract: Systems and methods for constructing and managing a unique road sign knowledge graph across various countries and regions is disclosed. The system utilizes machine learning methods to assist humans when comparing a new sign template with a plurality of stored sign templates to reduce or eliminate redundancy in the road sign knowledge graph. Such a machine learning method and system is also used in providing visual attributes of road signs such as sign shapes, colors, symbols, and the like. If the machine learning determines that the input road sign template is not found in the road sign knowledge graph, the input sign template can be added to the road sign knowledge graph. The road sign knowledge graph can be maintained to add signs templates that are not already in the knowledge graph but are found in real-world by integrating human annotator's feedback during ground truth generation for machine learning.

Abstract: Methods and systems of building a knowledge graph based on event-based ontology of a scene and vehicle trajectory in the scene. Image data corresponding to a plurality of scenes captured by one or more cameras is received. Event-based ontology data corresponding to events occurring in the plurality of scenes is received. Via an object-tracking machine-learning model, the system determines (i) a presence of a plurality of vehicles in the image data, and (ii) a plurality of vehicle trajectories, each vehicle trajectory associated with a respective one of the vehicles. Using a clustering model, the vehicle trajectories are clustered. A knowledge graph is augmented based on the clustered vehicle trajectories and the event-based ontology.

Abstract: Methods and system of building and augmenting a knowledge graph regarding ontology of events occurring in images. Image data corresponding to a plurality of scenes captured by one or more cameras is received. A knowledge graph is built with event-based ontology data corresponding to events occurring in the plurality of scenes. One or more of the scenes is displayed to a plurality of crowdsourcing workers which provide natural-language input including event-based semantic annotations corresponding to the scene. Using natural language processing on the input, triples are generated. The knowledge graph is augmented with the generated triples to yield an augmented knowledge graph for use in determining event-based ontology associated with the plurality of scenes.

Abstract: A videophone system for providing videophone service within narrow band digital network, based on pipelined processing elements, includes source codec means coupled to the image bus, including numerous processing elements of which processing element has DSP (Digital Signal Processor) module, local memory module coupled to the DSP module, FIFO (First Input First Output) memory module coupled to the DSP module and the local memory module, and image bus interface module coupled to the DSP module and the local memory module and the image bus, wherein communications between the processor elements is performed via the pipeline and common memory means coupled to the VME bus and the image bus, for storing message data for synchronization and communication between the processing elements.