Surveillance Vision Model Training With Synthetic Sensor Data
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Solution Overview
Problem
Existing surveillance systems require significant human intervention, are time and cost-intensive, and struggle with data collection and manual labeling for training neural networks, leading to inefficiencies and potential security risks.
Innovation Solution
A system and method for generating a synthetic training data set using spatially resolved sensor data to automatically train a machine learning computer vision model, allowing for efficient and user-friendly surveillance with reduced human effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data collection and labeling is used for training neural networks, then training data can be obtained, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent uses synthetic data generation to create copied representations of real-world scenarios through rendering engines. Instead of manually collecting and labeling real images, the system generates synthetic training data that replicates the visual characteristics and object properties needed for neural network training, dramatically reducing time and labor while maintaining data quality
Solution Approach 2:
The system employs automated pipelines where the rendering engine self-generates training data without human intervention. The neural networks are trained on this self-produced synthetic data, eliminating the need for manual data collection, annotation, and labeling processes that traditionally require significant human effort
2Reliability
If human surveillance personnel are deployed to monitor activities, then security monitoring can be performed, but operational costs and complexity increase
Solution Approach 1:
The patent replaces the mechanical system of human surveillance personnel with an automated computer vision system. Neural networks trained on synthetic data perform object detection, tracking, and anomaly detection automatically, substituting human operators and manual monitoring processes with algorithmic automation that reduces operational complexity and costs
Solution Approach 2:
The surveillance system performs self-monitoring through automated neural networks that continuously analyze sensor data without human intervention. The system autonomously detects threats, generates alerts, and maintains security monitoring, eliminating the need for human personnel to manually review footage and reducing system complexity
3Reliability
If real video streams are collected for training, then authentic training data can be obtained, but data collection effort and energy consumption increase significantly
Solution Approach 1:
Instead of collecting authentic real-world video streams which requires significant energy for recording, storage, and processing, the system creates synthetic copies of video data through rendering engines. These synthetic video streams replicate the visual and temporal characteristics needed for training while consuming minimal energy compared to real data collection processes
Data Source
AI summary
Technologies and techniques for automatically executing a security function of a surveillance system with respect to at least one object of interest based on spatially resolved sensor data for environment perception generated by at least one sensor device. Spatially resolved sensor data, is received. A trained machine-learning computer vision model is accessed to determine at least one surveillance parameter. The model includes a set of parameters set to values learned as a result of a training process based on a synthetic training data set, which is generated by rendering at least one model of the at least one object of interest according to at least one render parameter. The generation of the training data set is determined based on at least one environment parameter being characteristic for the environment to be surveilled and/or for the sensor device. The security function is executed depending on the at least one surveillance parameter.