Video Anonymization Through Reference-Entity Detection
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Solution Overview
Problem
Existing video anonymization systems using deep learning algorithms require extensive labeled data and are resource-intensive, making them inefficient and time-consuming for training and improving accuracy.
Innovation Solution
A system and method that utilizes processors to detect reference entities in video frames and anonymize portions where these entities are absent, employing techniques such as tagging, 3D volume projection, and tag filtering to efficiently protect sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms are used for anonymizing videos, then anonymization accuracy is improved, but training time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by detecting reference entities and determining their 3D positions and orientations before anonymization. This allows the system to pre-calculate region-of-interest boundaries and prepare anonymization masks in advance, reducing real-time processing requirements while maintaining high anonymization accuracy
Solution Approach 2:
The system introduces reference entities as intermediaries between the imaging device and the anonymization process. These reference entities serve as mediators that carry encoding information about the region of interest, enabling the system to accurately identify areas requiring anonymization without needing extensive training data, thus achieving high accuracy with reduced training time
2Measurement precision
If deep learning algorithms are used for anonymizing videos, then anonymization accuracy is improved, but computational resources required increase
Solution Approach 1:
The system replaces complex deep learning mechanical systems with a more efficient approach based on reference entity detection and 3D geometric calculations. By substituting the deep learning-based region identification mechanism with reference entity-based spatial reasoning, the system achieves comparable anonymization accuracy with significantly reduced computational resource requirements
Solution Approach 2:
The system extracts only the essential information needed for anonymization by detecting reference entities and their 3D positions, rather than processing entire video frames through resource-intensive deep learning models. This extraction approach focuses computational efforts on critical elements, reducing overall resource consumption while maintaining accuracy
3Reliability
If labeled data is collected and trained for diverse sensitive information types, then system effectiveness is improved, but time and resources for training increase
Solution Approach 1:
The system achieves universality by using reference entities that can represent multiple types of sensitive information (faces, medical records, identity marks, etc.) through a single unified detection and tracking framework. This multi-functional approach allows the system to effectively handle diverse sensitive information types without requiring separate training processes for each category, thereby improving system effectiveness while maintaining high training efficiency
Data Source
AI summary
A system and method for anonymizing videos is provided. The system comprises of a server, wherein the server comprises one or more processors. The one or more processors are configured to receive a video captured by an imaging device, wherein the video comprises of a plurality of video frames. The one or more processors are configured to analyse each of the video frames captured by the imaging device and detect reference entity in each of the video frames and anonymize at least a portion of each of the video frames in which the reference entity is absent.