Live Stream Content Filtering via Frame-Level Template Matching
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Solution Overview
Problem
Live streaming often includes prohibited content such as morally offensive material or secure information, which can deter viewers and complicate broadcast management, as human screening is prone to errors and has limited time for remedial action.
Innovation Solution
A method and system for filtering live stream content by defining a prohibited frame content template, analyzing frames, and comparing them against the template to detect prohibited content, with remedial actions such as image masking or blocking, using computer vision and machine learning processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human screening is used to filter prohibited content, then the system can identify inappropriate material, but the screening time is limited and errors may occur
Solution Approach 1:
The patent replaces the mechanical human screening process with an automated computer-based image analysis system. The system uses frame-level image processing, template matching, and machine learning algorithms to automatically detect prohibited content without human intervention, thereby eliminating time loss and human error while maintaining high filtering accuracy.
Solution Approach 2:
The patent implements preliminary action by pre-defining prohibited frame content templates and configuring the automated detection system before live streaming begins. This allows the system to be ready for immediate real-time filtering, eliminating the need for time-consuming manual screening during the broadcast while ensuring accurate detection of prohibited content.
2Measurement precision
If automated frame-level analysis is implemented, then prohibited content detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the video stream into individual frames for analysis. Each frame is processed independently through template matching and machine learning classification, allowing precise detection of prohibited content at the frame level while managing system complexity through modular processing of discrete image units rather than analyzing entire video sequences.
Solution Approach 2:
The patent uses copying by creating and storing prohibited frame content templates that represent known prohibited materials. These templates are copied and compared against incoming video frames using image processing algorithms, enabling accurate detection without requiring complex real-time analysis of every possible prohibited content variation.
3Reliability
If real-time filtering is performed, then prohibited content is prevented from being viewed, but processing speed requirements increase
Solution Approach 1:
The patent implements partial action by analyzing only specific regions of interest within video frames rather than processing every pixel uniformly. The system focuses computational resources on areas where prohibited content is most likely to appear, such as central regions or areas matching template patterns, thereby maintaining real-time prevention capability while reducing overall processing speed requirements.
Data Source
AI summary
A method of filtering images of live stream content may include defining a prohibited frame content template; analyzing live stream content at a frame level to determine content within each frame of the live stream content; and comparing a frame of the live stream content against the prohibited frame content template to detect prohibited content in the frame that matches prohibited frame content as defined by the prohibited frame content template.