Scene Transition Detection Using Object Confidence Measures
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Solution Overview
Problem
Existing scene detection algorithms in video processing face difficulties in detecting transitions, especially dissolve transitions, due to their reliance on low-level information and high processing requirements, and often require manual tuning and are not robust across varying video footage.
Innovation Solution
A computer-implemented method that analyzes confidence measures associated with detected objects in video frames to identify scene transitions, allowing for efficient detection of dissolve-type transitions and integration with existing video processing systems, leveraging high-level semantic information and reducing the need for manual tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing scene detection algorithms use low-level information (color histograms, brightness, contrast) to identify scene changes, then the detection process is simple to implement, but the detection accuracy deteriorates for dissolve transitions between scenes of similar visual characteristics
Solution Approach 1:
The patent changes the detection parameter from low-level visual features (color, brightness, contrast) to high-level semantic features (object detection confidence measures). This parameter transformation enables accurate detection of dissolve transitions by monitoring changes in object presence and confidence scores across frames, rather than relying on pixel-level comparisons that fail when scenes have similar visual characteristics.
2Device complexity
If existing scene detection algorithms are designed as standalone systems, then the algorithm structure is simple and independent, but the processing requirements increase significantly for large videos
Solution Approach 1:
The patent merges scene transition detection with object detection by reusing the object detector's confidence measures and outputs. Instead of running a separate standalone scene detection algorithm, the system integrates scene transition detection into the existing object detection pipeline, thereby reducing redundant processing and improving overall efficiency without increasing algorithmic complexity.
Solution Approach 2:
The object detection system is given multiple functions: it not only detects objects but also provides the basis for scene transition detection through its confidence measures. This multi-functionality eliminates the need for a separate dedicated scene detection system, reducing overall processing requirements while maintaining detection accuracy.
3Reliability
If existing scene detection methods rely on manual tuning for different video types, then the algorithm can be adapted to specific video characteristics, but the ease of operation deteriorates due to requiring manual intervention
Solution Approach 1:
The system achieves adaptability to different video types automatically through the use of object detection confidence measures, which inherently capture the semantic content and structure of various video genres. The algorithm requires no manual tuning or configuration - it self-adapts to different video characteristics by monitoring changes in object presence and confidence scores, eliminating the need for human intervention while maintaining high reliability across diverse video footage.
Data Source
AI summary
Concepts for detecting a scene transition in video footage are presented. One example comprises, for each of a plurality of different frames of video footage, obtaining a value of a confidence measure associated with a detected object in the frame. A scene transition in the video footage is then detected based on the obtained values of the confidence measure.