Sentinel Frame Detection via Pattern Analysis
Solution Overview
Problem
The manual process of identifying sentinel sequences for content transitions in videos is labor-intensive and difficult to train, making it challenging to automate the detection of ad boundaries without user supervision.
Innovation Solution
A system that uses pattern analysis to automatically detect sentinel frames by identifying sequences of frames based on visual features, such as color layout descriptors and edge histograms, allowing for the identification of transitions between content types without prior knowledge of the sentinel frames, enabling automated detection and replacement of ads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of sentinel frames is used, then accuracy of sentinel frame identification is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs automated sentinel frame detection using pattern recognition algorithms that analyze video frames independently without requiring manual user input. The algorithm automatically identifies sentinel frames by detecting specific patterns in frame sequences, eliminating the need for human operators to manually extract these frames while maintaining high accuracy through sophisticated pattern matching
Solution Approach 2:
The patent replaces the manual mechanical process of frame extraction with an automated computational system. Instead of human operators visually inspecting and extracting frames, the system uses computer vision algorithms and pattern recognition to automatically detect sentinel frames based on their distinctive visual patterns and sequence characteristics
2Measurement precision
If manual extraction process is used, then sentinel frames can be identified accurately, but ease of operation deteriorates due to difficulty in training users
Solution Approach 1:
The system is designed to operate autonomously without requiring trained human operators. The automated pattern recognition algorithm performs sentinel frame detection independently, eliminating the need for user training while maintaining high identification accuracy through its sophisticated analysis of frame patterns and sequences
3Productivity
If automated detection without user supervision is implemented, then productivity is improved, but reliability deteriorates due to lack of user supervision
Solution Approach 1:
The patent replaces manual supervision with an automated pattern recognition system that uses computer vision and machine learning algorithms to reliably detect sentinel frames. The system analyzes visual patterns, color layouts, and frame sequences to automatically identify sentinel frames with high reliability, eliminating the need for human supervisors while maintaining or improving detection accuracy
Data Source
AI summary
Particular embodiments extract features for frames from a video. Then, a sequence of frames is identified based on a pattern analysis of frames based on the features. This pattern may be used to select sentinel features from the sequence of frames. For example, a pattern may include a first sentinel sequence followed by a transitional frame and then a second sentinel sequence. The transitional frame may be a black frame that is used to identify back-to-back sentinel sequences. Then, a sentinel frame that demarks a transition from a first content type to a second content type is identified. For example, the frames on either side of the black frame may be very similar and be identified as sentinel frames that mark a transition from program content to advertisement content. The above process may allow automated detection of sentinel frames that can run without user supervision.