CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. patent application Ser. No. 16/853,031, filed Apr. 20, 2020, and entitled “SYSTEMS AND METHODS FOR ENHANCED REAL-TIME IMAGE ANALYSIS WITH A DIMENSIONAL CONVOLUTION CONCEPT NET,” the contents of which are hereby incorporated by reference, in their entirety and for all purposes, herein.

BACKGROUND

The field of the present disclosure relates generally to analyzing images in real-time and, more specifically, to a dimensional convolution concept net to perform real-time analysis of images and video to recognize objects and actions.

Evaluation of images is labor-intensive process and may be dependent on subject matter expertise. Furthermore, evaluation of video is even more labor intensive. One major problem with image and video analysis, such as facial recognition and workplace safety analysis is the combination of speed and accuracy that is required. While additional hardware may improve the processing for these applications, this can be an expensive proposition. This is especially true for situations where the sensor that is capturing the images or video is distance from the processing. The amount of network bandwidth required to transmit video may be significant, especially at higher resolution. Accordingly, it would be advantageous to be able to accurately and quickly process images for image recognition without requiring significant computing resources.

BRIEF DESCRIPTION

In one aspect, a system for analyzing images in real-time is provided. The system includes a computing device including at least one processor in communication with at least one memory device. The at least one processor is programmed to receive an image including a plurality of objects. The at least one processor is also programmed to detect the plurality of objects in the image. The at least one processor is further programmed to determine dependencies between each of the plurality of objects. In addition, the at least one processor is programmed to identify the plurality of objects based, at least in part, on the plurality of dependencies. Moreover, the at least one processor is programmed to determine one or more objects of interest from the plurality of identified objects.

