Object Recognition Encryption for Privacy-Preserving Surveillance

The system performs preliminary actions by pre-training the neural network model with large amounts of data before actual surveillance operation. This pre-training phase prepares the model to achieve high detection accuracy in the deployment phase without requiring complex real-time processing, thus resolving the contradiction between accuracy and operational complexity.

The patent uses a copied approach by training the neural network on synthesized or pre-collected image data that replicates real surveillance scenarios. This allows the system to learn from extensive training data without requiring equally extensive real-time processing resources during actual surveillance, maintaining high accuracy while managing system complexity.

The neural network model undergoes extensive pre-training before deployment, performing the computationally intensive learning phase in advance. Once trained, the model can perform object detection with high accuracy during surveillance operations with significantly reduced computational overhead, as the heavy lifting of pattern recognition has already been accomplished during training.

The system employs self-service mechanisms through automated object detection and classification by the neural network, eliminating the need for manual review of surveillance footage. The model autonomously identifies and flags regions containing personal information, reducing the computational and human resources required for privacy protection while maintaining high accuracy.

The system implements self-service automation where the neural network automatically detects objects containing personal information and triggers the mosaic processing function without human intervention. This automated pipeline maintains high productivity by continuously processing surveillance footage in real-time, while achieving complete automation of the privacy protection workflow from detection to obfuscation.

The system incorporates feedback mechanisms where the neural network continuously monitors surveillance footage, identifies regions requiring privacy protection, applies mosaic processing, and verifies the effectiveness of the obfuscation. This closed-loop feedback system ensures high automation while maintaining processing efficiency by only applying processing where needed and verifying results automatically.