Method and system for analyzing live broadcast video content with a machine learning model implementing deep neural networks to quantify screen time of displayed brands to the viewer

A data processing system comprising a processor and a non-transitory data storage comprising computer-readable instructions that, when executed by the processor, perform an automated process comprising:

• receiving a video stream comprising a series of digital images;
• automatically extracting an object of interest associated with one or more brand features from the series of digital images by applying a trained brand recognition model that comprises a first neural network to one or more of the digital images to detect the one or more brand features, wherein the object of interest is associated with a brand;
• creating a report of results comprising the brand associated with the object of interest extracted by the trained brand recognition model and at least one time information related to the detection of the object of interest in the video stream;
• and
• providing the report of results as an output from the data processing system.

An automated process to be performed by a data processing system to automatically recognize brand imagery in a video stream, the automated process comprising:

• receiving the video stream comprising a series of digital images;
• automatically extracting an object of interest associated with one or more brand features from the series of digital images by applying a trained brand recognition model that comprises a first neural network to one or more of the digital images to detect one or more brand features, wherein the object of interest is associated with a brand;
• creating a report of results comprising the brand associated with the object of interest extracted by the trained brand recognition model and at least one time information related to the detection of the object of interest in the video stream;
• and
• providing the report of results as an output from the data processing system.