ASIC Computer Vision Metadata Pipeline for Private Home Tracking
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Solution Overview
Problem
Existing computer vision systems for home environments are either too simple and unpredictable or too complex and uneconomical, with high computational and storage costs that scale linearly with the number of users, cameras, and resolution, lacking accuracy and scalability, and failing to provide real-time processing and privacy guarantees.
Innovation Solution
A computer-vision system that generates a digital representation of people or objects from pixel streams, determines attributes, and controls networked devices, using an ASIC-based engine for real-time metadata processing without continuous video output, capable of tracking multiple individuals and reconstructing 3D approximations for reliable tracking across multiple sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated video analysis is performed on servers, then analysis accuracy is improved, but system complexity and cost increase linearly with the number of users, cameras and resolution
Solution Approach 1:
The system segments video analysis into two parts: simple motion detection performed locally by PIR sensors in each room, and sophisticated analysis performed centrally by a server only when motion is detected. This segmentation allows accurate analysis only when needed, reducing overall system complexity while maintaining precision.
Solution Approach 2:
The system performs preliminary motion detection using low-power PIR sensors before triggering sophisticated video analysis. This preliminary action filters out unnecessary analysis cases, reducing the computational burden on the server and allowing high-accuracy analysis to be performed only when actually needed.
2Measurement precision
If full-frame video is transmitted to remote servers for analytics, then analysis capability is improved, but bandwidth consumption and storage costs increase linearly
Solution Approach 1:
The system extracts only the essential information (motion detection events) from the video stream at the edge devices before transmitting to the server. Instead of transmitting full-frame video, only relevant motion events are sent, dramatically reducing data transmission volume while preserving analytics capability.
Solution Approach 2:
Motion detection is performed preliminarily at the edge devices before video transmission. This preliminary processing extracts only the necessary information, allowing the server to perform analytics on compact motion event data rather than raw video streams, reducing bandwidth and storage requirements.
3Productivity
If simple video analysis is performed in cameras, then processing speed is improved, but analysis accuracy and reliability deteriorate
Solution Approach 1:
The system segments the analysis task between edge devices (simple motion detection for speed) and central server (sophisticated analysis for accuracy). This segmentation allows each component to perform its designated function optimally, maintaining both processing speed and analysis accuracy.
Solution Approach 2:
The system introduces an intermediary motion detection layer that bridges simple camera-based detection and sophisticated server-based analysis. This intermediary filters and preprocesses data, enabling fast initial processing while ensuring accurate final analysis through server validation.
4Adaptability or versatility
If cloud cameras transmit full video streams, then remote monitoring capability is improved, but privacy guarantees are compromised
Solution Approach 1:
The system extracts only essential motion detection data from video streams for remote transmission, leaving detailed video content locally. This extraction approach enables remote monitoring capability while minimizing privacy risks by not transmitting sensitive visual information.
Solution Approach 2:
The system creates simplified copies (motion events) of the original video data for remote transmission instead of transmitting the actual video content. This copying approach maintains monitoring capability while protecting privacy by keeping the original detailed video data local.
Data Source
AI summary
The field of the invention relates to computer vision systems and methods providing real time data analytics on detected people or objects in the home environment or other environments. It is based on an embedded engine that analyses an image from a raw sensor and virtualised the image into a digital representation enabling a digital understanding of the environment while guarantying privacy. It comprises multiple image processing blocks and embedded firmware.