Semi-supervised Object Recognition Model Clustering
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Solution Overview
Problem
Current surveillance systems require substantial ground truth data for effective object recognition, which is often specific to a user's environment and time-consuming to collect, leading to inefficiencies in training site-specific object recognition models.
Innovation Solution
The implementation of semi-supervised learning techniques using clustering methods to reduce the number of queries needed for ground truth verification, allowing for the generation of site-specific object recognition models by clustering images and using representative images to verify clusters, thereby reducing the amount of user feedback required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If substantial ground truth data is collected for training site-specific object recognition models, then detection accuracy is improved, but data collection time and user effort increase significantly
Solution Approach 1:
The patent segments the ground truth data into two categories: verified ground truth (user-confirmed) and pseudo ground truth (cluster-assumed). By dividing the training data in this way, the system can use a small amount of verified data to validate cluster centers, while automatically generating pseudo ground truth for the remaining training samples, thereby reducing user verification effort while maintaining detection accuracy
Solution Approach 2:
The patent performs preliminary clustering of detection images before user verification. By pre-organizing images into clusters based on similarity metrics, the system reduces the number of images that require user verification to only the cluster center representatives, significantly reducing data collection time while preserving the ability to achieve high detection accuracy through the pseudo ground truth generation mechanism
2Measurement precision
If user verification is performed on all detection images, then ground truth accuracy is improved, but user effort and query volume increase
Solution Approach 1:
The patent implements a self-service mechanism where the clustering algorithm automatically generates pseudo ground truth labels for training images based on verified cluster centers. This allows the system to self-generate training data without requiring users to manually verify every image, thereby maintaining ground truth accuracy while dramatically reducing user effort
Solution Approach 2:
The patent applies partial verification by only requiring user confirmation of cluster center images rather than all detection images. This partial action approach is sufficient to establish accurate cluster representations, which then automatically propagate to generate pseudo ground truth for the entire training set, reducing user effort while preserving ground truth accuracy
3Measurement precision
If site-specific object recognition models are trained with environment-specific data, then detection precision for site-specific objects is improved, but model adaptation complexity increases
Solution Approach 1:
The patent creates a universal training framework that can adapt to any site-specific environment through automatic clustering. The same clustering and pseudo ground truth generation process works across different environments and object types, making the system universally applicable while maintaining site-specific detection precision, thereby reducing model adaptation complexity
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for semi-supervised training of an object recognition model. The methods, systems, and apparatus include a monitoring system including a camera located at a property and configured to generate images and one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform actions of determining a cluster of images meets a threshold for number of included images and a threshold for cluster tightness. A representative image of the cluster is selected and a query including the representative image of the cluster is provided. User feedback responsive to the query is received and an object recognition model is updated based on the user feedback.