SEMANTIC REPRESENTATION MODULE OF A MACHINE-LEARNING ENGINE IN A VIDEO ANALYSIS SYSTEM

摘要

A machine-learning engine is disclosed that is configured to recognize and learn behaviors, as well as to identify and distinguish between normal and abnormal behavior within a scene, by analyzing movements and/or activities (or absence of such) over time. The machine-learning engine may be configured to evaluate a sequence of primitive events and associated kinematic data generated for an object depicted in a sequence of video frames and a related vector representation. The vector representation is generated from a primitive event symbol stream and a phase space symbol stream, and the streams describe actions of the objects depicted in the sequence of video frames.

主权项

1. A system for processing data describing a scene depicted in a sequence of video frames, the system comprising:
a processor; and a memory communicatively coupled to the processor, the memory comprising computer-readable instructions that, when executed by the processor, cause the system to: identify one or more objects detected in the scene; receive input data associated with the one or more identified objects; evaluate the received input data to identify one or more primitive events, wherein for a first one of the primitive events, a semantic value is provided describing a behavior engaged in by a first one of the objects depicted in the sequence of video frames and wherein the first one of the primitive events has an assigned primitive event symbol; generate, for the first object, a primitive event symbol stream which includes the primitive event symbol corresponding to the first one of the primitive events identified for the first object; and output the primitive event symbol stream.