Attention masks in neural network video processing

Abstract

A device configured for more efficiently processing video images within a set of video image data to detect objects is described herein. The device may include a processor configured to execute a neural network such as a convolutional neural network. The device can receive video image data from a plurality of cameras, such as stationary cameras. The device can acquire a set of sample images from a stationary camera and submit them to a specialized neural network for processing to generate an attention mask. The attention mask can be generated from a variety of methods and is applied to each of the subsequently acquired images form the camera to narrow down areas where the convolutional neural network should process data. The application of attention masks to images within video image data creates masked images that can be processed to detect objects with much greater accuracy and fewer computational resources required.