Self-Configuring Video Analytics via Semantic Segmentation
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Solution Overview
Problem
Current video analytics systems require labor-intensive configuration processes, making them costly and inaccessible for end-users to install and configure, especially for environments that need specific detection tasks.
Innovation Solution
A self-configuring video analytics system using a semantic segmentation engine trained with labelled images from initial sites, allowing for no-configuration object recognition at new sites through image data repositories and remote verification, which automatically adjusts settings based on observed objects and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration process is used for video analytics system, then detection accuracy can be optimized for specific environments, but installation complexity and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training the semantic segmentation engine using labelled images from multiple initial sites before deployment. This pre-training phase captures diverse environmental characteristics and object patterns, enabling the system to adapt quickly to new sites without requiring extensive manual configuration or on-site training data collection.
Solution Approach 2:
The system implements self-service through automatic environment adaptation mechanisms. The semantic segmentation engine automatically adjusts to new environments by processing images from the new site and modifying its parameters without human intervention. The system self-configures by analyzing local characteristics and optimizing detection parameters autonomously.
2Reliability
If extensive training and manual adjustments are required, then system performance can be optimized, but ease of installation and operation deteriorates
Solution Approach 1:
The system performs self-configuration and self-optimization automatically. The semantic segmentation engine processes images from the new site, identifies environmental characteristics, and adjusts its parameters without requiring user training or manual intervention. This maintains high system performance while dramatically simplifying installation and operation for end users.
Solution Approach 2:
The system performs preliminary training actions using diverse labelled images from multiple initial sites before deployment. This pre-training establishes a robust foundation that enables reliable performance in new environments without requiring extensive on-site configuration or user expertise.
3Measurement precision
If site-specific training is performed manually, then object recognition accuracy improves, but installation time and cost increase
Solution Approach 1:
The system performs preliminary training using labelled images from multiple initial sites before deployment to the new site. This pre-training phase captures diverse object patterns and environmental characteristics, enabling the system to achieve high object recognition accuracy quickly at the new site without requiring extensive manual training or on-site data collection.
Solution Approach 2:
The semantic segmentation engine is trained on diverse labelled images from multiple different sites and environments, making it universally adaptable. This multi-site training approach creates a robust model that can recognize objects accurately across various environments without requiring site-specific manual training, reducing installation time while maintaining high accuracy.
4Manufacturing precision
If configuration process requires expert knowledge, then detection tasks can be optimized, but accessibility for end-users decreases
Solution Approach 1:
The system performs self-configuration and automatic optimization of detection tasks without requiring expert knowledge. The semantic segmentation engine automatically analyzes images from the new site, identifies environmental characteristics and object patterns, and adjusts its parameters autonomously. This enables end-users with no specialized training to deploy and optimize video analytics systems effectively.
Solution Approach 2:
The system performs preliminary training on diverse labelled images from multiple sites before deployment, pre-loading expert-level detection capabilities. This allows the system to automatically optimize detection tasks for specific environments without requiring user expertise, making advanced video analytics accessible to ordinary end-users.
Data Source
AI summary
A computerized method for providing no-configuration object recognition video analytics, the method comprising generating a database stored in computer memory and including labelled images of recognized objects imaged at an initial set of sites including at least one site; using the database as a training set to train a pattern recognition engine; uploading images of additional site/s including at least one additional site which is not a member in the initial set of sites; providing object recognition results for the additional site/s by using the pattern recognition engine on the images as uploaded; verifying the object recognition results; and using a processor for operating the pattern recognition engine on images generated at each the additional site/s including modifying the pattern recognition engine according to and when indicated by the verifying, thereby to provide no-configuration object recognition video analytics at the additional site/s.