Dimensional Convolution Concept Nets for Low-Bandwidth Analysis
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Solution Overview
Problem
Existing image and video analysis technologies face challenges in achieving high-speed and accurate processing without requiring significant computing resources, especially in scenarios where sensors are distant from processing units, leading to high network bandwidth demands.
Innovation Solution
A Dimensional Convolutional Concept Net (DCCN) computing device that analyzes images in real-time by detecting and tracking objects, determining dependencies, and identifying objects of interest, using a neural network with an end-to-end AI model that learns to recognize objects and actions through unsupervised learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If additional hardware is used to improve processing speed and accuracy, then the processing capability is improved, but the cost increases significantly
Solution Approach 1:
The patent segments the image processing task into multiple stages: initial processing at the edge device (camera module) for quick filtering, followed by cloud-based processing for comprehensive analysis. This segmentation allows the system to achieve high processing speed for common tasks while maintaining accuracy through distributed computing, avoiding the need for expensive local hardware upgrades.
Solution Approach 2:
The patent introduces a cloud-based processing service as an intermediary between the camera module and the final analysis. The cloud service receives images from edge devices, performs comprehensive analysis using powerful computing resources, and returns results. This intermediary approach enables high processing capability without requiring expensive local hardware at the edge device.
2Area of stationary object
If sensors are placed distant from processing units, then the system can monitor wider areas, but the network bandwidth requirement increases significantly
Solution Approach 1:
The patent extracts and processes only the most critical information from images at the edge device before transmitting to the cloud. By performing initial object detection and classification locally, the system transmits only relevant data (object locations, types, confidence scores) rather than raw images, dramatically reducing network bandwidth requirements while maintaining comprehensive monitoring capability.
Solution Approach 2:
The patent applies partial processing at the edge device level, performing only the necessary computations to identify and filter objects of interest. This partial action approach processes data locally to reduce transmission volume, while the cloud provides comprehensive analysis when needed, balancing bandwidth efficiency with analytical depth.
3Speed
If real-time processing is implemented, then the response speed is improved, but the computational requirements increase
Solution Approach 1:
The patent segments computational tasks between edge devices and cloud servers. Edge devices perform fast, low-computation tasks like basic object detection and filtering to achieve real-time response. Complex analysis requiring high computational power is offloaded to the cloud, enabling real-time processing without demanding excessive local computational resources.
Solution Approach 2:
The patent implements periodic processing where the system continuously monitors and processes images at optimal intervals rather than continuously at maximum computational capacity. This allows real-time responsiveness for critical events while reducing overall computational load through intelligent timing and prioritization of processing tasks.
Data Source
AI summary
A system for analyzing images is provided. The system includes a computing device having 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, detect the plurality of objects in the image, determine dependencies between each of the plurality of objects, identify the plurality of objects based, at least in part, on the plurality of dependencies, and determine one or more objects of interest from the plurality of identified objects.