Self-Training Video Analytics for Rare Targets and Data Privacy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video analytics systems lack comprehensive methods for deep learning, are not user-friendly, require extensive user intervention, and fail to ensure data privacy and security, particularly for rare and confidential data.
Innovation Solution
A customized self-training machine learning system for video analytics that includes a data processing module, data annotation, automatic model training, model verification, model deployment, and a user operation interface, ensuring privacy and security through network-attached storage and scalable computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning methods are used for video analytics, then analysis capability is improved, but system complexity and user operation difficulty increase
Solution Approach 1:
The system implements self-service through automated model training where the system trains itself using uploaded videos without requiring user programming knowledge. The automatic model training module handles all technical operations including data processing, model selection, and parameter optimization, allowing users to simply upload videos and receive analysis results
Solution Approach 2:
The patent introduces an intermediary layer between users and complex deep learning systems. The automatic model training module acts as a mediator that translates simple user video uploads into processed training data and generates appropriate models automatically, shielding users from underlying technical complexities
2Productivity
If existing video analytics systems are used, then video analysis function is provided, but data privacy and security cannot be guaranteed
Solution Approach 1:
The system segments the video analysis process into local processing and selective cloud training. Videos are processed locally on user devices for common tasks, while only necessary training data is selectively uploaded to cloud servers for model optimization, minimizing data exposure risks
Solution Approach 2:
The patent uses synthetic data generation to create virtual copies of real video data for training purposes. The synthetic data generator creates artificial training samples that capture essential patterns without containing actual sensitive information, allowing model training without exposing real confidential videos
3Adaptability or versatility
If manual model training is required, then model customization is achieved, but time consumption and resource requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing uploaded videos automatically, extracting key frames, detecting objects, and preparing training data before actual model training begins. This preliminary processing significantly reduces the time and computational resources needed for subsequent model training
Solution Approach 2:
The automatic model training module dynamically adjusts training parameters including learning rate, batch size, and model architecture based on the characteristics of uploaded videos and available computational resources, optimizing training efficiency without manual intervention
Data Source
AI summary
Disclosed is a customized self-training machine learning system for video analytics for rare targets. The customized self-training machine learning system has a data processing module, a data annotation module with a labeling module, an automatic model training module configured to self-train the model based on the user's desire to detect rare targets, a model verification module with automatic error analysis and label approval to optimize the model, a model deployment module coupled with a user operation interface module, and a video analysis module based on the trained rare targets.