State-Change Tracking in User-Customized Vision Event Detection
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Solution Overview
Problem
Existing computer vision systems lack the ability to provide user-customized event detection, requiring extensive computational resources and failing to adapt to individual user-defined events efficiently.
Innovation Solution
A system utilizing lightweight machine learning models, such as logistic regression and transformer-based encoders, allows users to define custom events with multiple states, enabling efficient detection and triggering actions based on state changes in image data, deployable on edge devices with minimal resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing computer vision systems are used for event detection, then detection capability is provided, but computational resources are excessively consumed and user customization is lacking
Solution Approach 1:
The system segments the computer vision processing into two distinct components: a pre-trained encoder model that extracts general visual features, and a user-customizable classifier that detects specific events. This segmentation allows the heavy feature extraction to be performed once, while the lightweight custom classification adapts to user needs with minimal computational overhead.
Solution Approach 2:
The encoder model is pre-trained on large datasets to learn general visual representations before deployment. This preliminary action enables the model to capture essential features in advance, so that during runtime, only lightweight classification operations are needed, significantly reducing real-time computational resource consumption while maintaining detection accuracy.
2Productivity
If lightweight models are used for edge deployment, then resource consumption is reduced, but detection accuracy may be compromised
Solution Approach 1:
The system merges the strengths of pre-trained deep learning models (feature extraction capability) with lightweight custom classifiers (adaptability and efficiency). The encoder model provides robust feature representations, while the simple classifier adapts to specific user-defined events, achieving both high accuracy and computational efficiency in a unified architecture.
Solution Approach 2:
The system changes the parameters of the classification layer to match user-defined events while keeping the encoder parameters fixed. This allows the model to adapt to different detection tasks by simply adjusting the classification parameters rather than retraining the entire model, maintaining accuracy while enabling efficient edge deployment.
Data Source
AI summary
Techniques are generally described for user-customized computer vision event detection. A camera device may capture a first frame of image data. A first machine learning model may generate first embedding data for the first frame of image data. The first embedding data may be input into a second machine learning model associated with the camera device. The second machine learning model and the first embedding data may be used to determine that an area-of-interest represented by the first frame of image data is in a first state. State data stored in memory may be determined. The state data may indicate that the area-of-interest was previously in a second state. An alert may be sent to a device associated with the camera device indicating that the area-of-interest has changed from the second state to the first state.