Automated Video Redaction With Person Tracking for Privacy Compliance
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Solution Overview
Problem
Existing video surveillance systems lack efficient and accurate methods for automatically redacting identifying images of individuals, which is necessary to comply with privacy regulations such as GDPR, as manual redaction is time-consuming and imprecise.
Innovation Solution
A system and method for automated video redaction that includes object detection, bounding region determination, object tracking, and user interface for selection, using machine learning techniques to identify and replace pixels within bounding regions of individuals in video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual redaction methods are used to remove identifying images from video surveillance footage, then redaction can be performed, but the process is time-consuming and imprecise
Solution Approach 1:
The patent replaces manual mechanical redaction processes with an automated computer vision system that uses machine learning models to detect, track, and redact identifying images. The system automatically processes video frames through object detection, bounding region determination, and pixel replacement operations, eliminating the need for manual frame-by-frame redaction while achieving both high efficiency and precision through algorithmic accuracy
Solution Approach 2:
The system enables self-service redaction by automatically detecting identifying images and performing redaction without human intervention. The computer vision pipeline autonomously processes surveillance footage, identifies persons of interest, determines appropriate bounding regions, and applies redaction algorithms, allowing the system to serve its own redaction needs without external manual operation
2Productivity
If automated redaction systems are implemented to improve efficiency, then redaction speed increases, but system complexity increases
Solution Approach 1:
The patent segments the redaction system into distinct functional modules: object detection module, bounding region determination module, object tracking module, and redaction execution module. Each module performs a specific task in the redaction pipeline, allowing for independent optimization, easier maintenance, and modular complexity management while achieving high overall system efficiency through coordinated operation of these specialized components
Data Source
AI summary
Aspects of the embodiments described herein are related to systems, methods, and computer products for performing video redaction. When performing video redaction, a video is received and converted into a plurality of frames. For each of the frames, it is detected if one or more people are present in at least one of the plurality of frames. Bounding regions are determined for the detected people in each frame. The bounding regions are tagged with an identifier identifying the person associated with each bounding region. An icon identifying the detected person is determined and displayed on an interface as a selectable input, wherein each selectable input is selectable to redact or keep the detected person in the video. Once a selection to redact a person is received, the bounding regions of the selected person are filled with replacement pixels. The plurality of frames are then converted into a new video.