Edge Visual and Auditory Observer for Low-Bandwidth HD Analysis
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Solution Overview
Problem
Existing video analytics systems face challenges in adapting to environmental changes, require high bandwidth, are inefficient in resource utilization, and lack integration of contextual information, leading to high costs and latency issues.
Innovation Solution
A multi-purpose intelligent observer system using multitask embedded deep learning technology that mimics human visual and auditory systems, performing cognitive analysis on the network edge with adaptive algorithms and cloud-based refinement, enabling real-time detection, tracking, and behavioral understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high definition video streaming is used for video analytics, then video quality and clarity are improved, but bandwidth requirements become prohibitively high making it expensive and unreliable
Solution Approach 1:
The patent extracts only the essential information from video frames using deep learning models (object detection, face recognition, behavioral analysis) rather than transmitting the complete video stream. This allows sending only annotated data (object locations, identities, actions) instead of full HD video, dramatically reducing bandwidth requirements while maintaining analytical accuracy.
Solution Approach 2:
The system segments the video processing task into multiple components: local edge devices perform initial frame analysis, cloud platform performs comprehensive analytics, and only necessary results are transmitted. This segmentation allows quality preservation at the source while minimizing data transmission over the network.
2Measurement precision
If discrete complex neural networks are used for specific functions like object detection and face recognition, then detection accuracy is improved, but system complexity and processing time increase significantly
Solution Approach 1:
The patent implements a universal deep learning framework that can perform multiple functions (object detection, face recognition, tracking, behavioral analysis) using a single integrated architecture rather than separate discrete networks for each function. This multi-functional approach maintains high accuracy while reducing overall system complexity and computational overhead.
Solution Approach 2:
The system pre-trains deep learning models offline with extensive data, then deploys these pre-trained models on edge devices for real-time inference. This preliminary training action allows the models to make accurate predictions without requiring complex real-time learning computations, reducing processing time and system complexity during actual operation.
3Measurement precision
If frame-by-frame analysis using neural networks is performed, then detection accuracy is improved, but processing time becomes too long to run in real time
Solution Approach 1:
Instead of continuously processing every single frame, the system uses periodic action by detecting motion changes and only processing frames where significant events occur. The deep learning models analyze frames at strategic intervals rather than continuously, maintaining detection accuracy while dramatically reducing processing time to enable real-time operation.
Solution Approach 2:
The patent implements dynamic processing where the system adjusts its analysis intensity based on scene conditions. When the scene is static, the system reduces processing frequency; when motion or events are detected, it increases processing intensity. This dynamic adaptation maintains accuracy when needed while improving overall processing speed during normal conditions.
4Device complexity
If non-adaptive algorithms are used for specific scenes, then algorithm simplicity is maintained, but effectiveness deteriorates when environment changes
Solution Approach 1:
The patent uses parameter changes by training deep learning models with transformation-invariant parameters that can automatically adapt to different environments, lighting conditions, and perspectives. The models learn to recognize objects and patterns regardless of environmental variations, maintaining effectiveness across diverse scenes without requiring complex adaptive algorithms or manual retraining for each environment.
Data Source
AI summary
An edge device for using a Multitask Embedded Deep Learning Technology to perform cognitive analysis over high definition video at real time without streaming the video frames to cloud processing. The technology mimics human multi-level cortex function. The device comprises a video camera, at least one embedded processor for analyzing digital video signal output from the video camera, a mission control program module for translating user instructions into configuration parameters, a vision analysis program module including at least one pre-trained ML model within the embedded processor, an object segmentation program module comprising a plurality of MLU, a behavior analysis program module including independent neural network backbones coupled to a neural network top layer. The plurality of MLU comprises a simple neural network backbone, a fully connected network (FCN), a standard regressor FCN network, and a recurrent neural network (RNN).