Security Camera AI Calibration for Scene-Specific Video Analytics
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Solution Overview
Problem
Security cameras deployed in diverse locations face challenges in accurately identifying objects and events due to varying scenes, as a single pre-trained AI model may not adequately adapt to different environments.
Innovation Solution
Implementing a scene-specific video analytics model trained on a security camera by capturing a calibration video with a human moving about the scene, using a motion detection algorithm to learn human features, and selecting the best AI model based on accuracy scores, with retraining by a remote server when classifications mismatch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single pre-trained AI model is used in all security cameras, then device complexity is reduced, but video analytics accuracy deteriorates due to inability to adapt to different scenes
Solution Approach 1:
The patent divides the AI model into two components: a pre-trained base model and a scene-specific fine-tuned model. Each security camera system segments the model training process by capturing local calibration data and training a dedicated fine-tuned model for that specific scene, while using the same pre-trained base model across all cameras. This segmentation resolves the contradiction by enabling scene-specific accuracy without requiring completely different models for each camera.
Solution Approach 2:
The patent applies local quality by training scene-specific fine-tuned models that are customized to each camera's unique environment (indoor, outdoor, specific locations), while maintaining a universal pre-trained base model. The fine-tuned models capture local scene characteristics such as lighting conditions, background objects, and spatial layouts, improving analytics accuracy for each specific scene without sacrificing the benefits of a shared model architecture.
2Measurement precision
If scene-specific AI models are trained for each camera, then video analytics accuracy is improved, but device complexity increases due to multiple models and calibration processes
Solution Approach 1:
The patent implements universality through a shared pre-trained base model that serves all security cameras across different locations and scenes. The base model is trained on diverse data and performs the core object detection and recognition functions universally. Scene-specific fine-tuned models then specialize in local adaptations, creating a multi-functional system where the base model provides universal functionality and fine-tuned models add scene-specific capabilities.
Solution Approach 2:
The patent applies preliminary action by pre-training the base AI model on a comprehensive dataset before deployment to specific scenes. This preliminary training establishes a solid foundation of general object recognition and detection capabilities that can be efficiently fine-tuned for specific scenes. The calibration process captures local scene characteristics and uses them to fine-tune the model, leveraging the pre-trained knowledge to reduce the complexity of scene-specific adaptation.
3Measurement precision
If multiple AI models are stored and tested in each camera, then the best model selection is improved, but loss of time increases due to model testing and calibration procedures
Solution Approach 1:
The patent applies parameter changes by fine-tuning the pre-trained base model using calibration data specific to each scene. Instead of testing multiple completely different models, the system adjusts the parameters of a single base model architecture to adapt to local scene characteristics. This parameter adjustment approach reduces the time required for model selection while maintaining high accuracy, as the fine-tuning process builds upon existing pre-trained parameters rather than evaluating entirely different models.
Data Source
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AI summary
A security camera is calibrated by capturing a video of a human moving about a scene. A motion detection algorithm is used to detect the human and a scene specific AI classifier model is trained. One or more AI models is applied to the video and an accuracy score for each of the AI models is determined. The AI model with the highest accuracy score is selected. Detected motion is classified as human or non-human using the scene specific AI classifier model and the selected best AI model. When the classification of the scene specific AI classifier model and the selected best AI model do not match, the selected best AI model is retrained at the remote server using a server hosted AI model as ground truth, and the retrained best AI Model is then sent from the remote server to the security camera for subsequent use.