Vision System for Person Classification Using Vector Probability Models
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Solution Overview
Problem
Conventional security systems struggle to efficiently classify individuals based on visual appearance and dwell locations due to the complexity of setting up object detection capabilities, which often rely on pre-trained machine learning algorithms limited to specific environments and prone to human error.
Innovation Solution
A self-learning security system that uses computer vision to classify persons by receiving image frames from cameras, identifying and vectorizing images of persons, generating a probability distribution of vector representations, and identifying an associate vector representation through sampling, allowing for automatic classification without the need for extensive human intervention or machine learning model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a pre-trained machine learning algorithm is used to identify a particular uniform, then the security system can automatically classify persons, but the effectiveness is limited to environments that use that particular uniform
Solution Approach 1:
The system performs self-training by automatically collecting images of persons in the environment, generating vector representations, and iteratively improving its classification model without human intervention. This self-service mechanism enables the system to adapt to any environment regardless of uniform type, resolving the contradiction between automation and environmental versatility
Solution Approach 2:
The system transforms visual appearance data into vector representations and uses probability distributions to model associate characteristics. By changing the parameter space from raw pixel data to vector embeddings and probability models, the system achieves both automatic classification and universal adaptability across different environments
2Adaptability or versatility
If a machine learning algorithm is programmable by a system administrator through manual generation of training dataset, then the system can be customized, but the setup becomes lengthy, cumbersome, and prone to human error
Solution Approach 1:
The system eliminates the need for administrator intervention by automatically collecting training images, generating vector representations, and iteratively improving its classification model. This self-service approach removes the time-consuming manual setup process while maintaining full system customization to the specific environment
Solution Approach 2:
The system performs preliminary automatic data collection and model training during an initial period before deployment. By completing the training process automatically in advance, the system eliminates setup time while ensuring the model is customized to the specific environment's characteristics
3Adaptability or versatility
If the quality of the machine learning algorithm depends on how well the system administrator generates the training dataset, then the system can be customized, but the reliability becomes dependent on administrator expertise
Solution Approach 1:
The system automatically generates high-quality training data by collecting images during operation and uses iterative probability modeling to improve classification reliability. This self-service mechanism eliminates dependence on administrator expertise while maintaining full customization to the environment
Solution Approach 2:
The system uses probability distributions to model associate characteristics and continuously refines its classification model based on observed data. This feedback mechanism ensures high classification reliability by statistically identifying patterns in associate appearance and behavior, independent of administrator skill level
Data Source
AI summary
Example implementations include a method, apparatus and computer-readable medium of computer vision configured for person classification, comprising receiving, during a first period of time, a plurality of image frames of an environment, identifying images of persons from each frame of the plurality of image frames, and determining a respective vector representation of each of the images. The implementations include generating a probability distribution indicative of a likelihood of a particular vector representation appearing in the plurality of image frames and identifying an associate vector representation by sampling the probability distribution using a probability model. The implementations include determining an input vector representation of an input image identified in an image frame depicting a person and received during a second period of time. The implementations include comparing the input vector representation with the associate vector representation and classifying, based on the comparison, the person in the input image as an associate.