Scene Change Detection Using Visual Classification Functions
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Solution Overview
Problem
Current methods for detecting scene changes in video streams are inefficient and prone to false detections due to susceptibility to changes in viewing angle, zoom level, and lighting conditions, and fail to effectively segment frames into coherent scenes.
Innovation Solution
A computer-implemented method and system that analyze content differences between consecutive frames using trained visual classification functions to detect scene changes by generating semantic image descriptors and comparing probability scores, with a pre-defined threshold optimized for accurate detection, and the ability to group frames into macro-scenes based on content similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional scene change detection methods are used, then detection speed is maintained, but detection accuracy deteriorates due to susceptibility to changes in viewing angle, zoom level, and lighting conditions
Solution Approach 1:
The patent transforms video frames into a different parameter space using Gabor wavelet transforms and histograms of oriented gradients. Instead of comparing raw pixel values directly, the system converts visual information into frequency-domain representations and gradient orientation distributions, making the comparison invariant to lighting changes, zoom levels, and viewing angle variations. This parameter transformation resolves the contradiction by maintaining detection accuracy while achieving reliability under varying conditions.
Solution Approach 2:
The patent replaces traditional mechanical/image-based comparison methods with mathematical signal processing techniques. Instead of directly comparing frame images or pixel values, the system uses Gabor wavelet transforms and histogram comparisons in the frequency and gradient domains. This substitution of the detection mechanism eliminates sensitivity to visual transformations while maintaining accurate scene change detection.
2Measurement precision
If frame-by-frame comparison is performed, then detection thoroughness is improved, but processing time increases
Solution Approach 1:
The patent segments the video stream into key frames for processing, using I-frames (intra-coded frames) as the basis for scene change detection. By processing only these independently encoded frames rather than every frame in the video stream, the system maintains thorough detection capability while significantly reducing processing time. The segmentation principle allows the system to achieve comprehensive scene change detection without the computational burden of analyzing every single frame.
Data Source
AI summary
A computer implemented method of detecting scene changes in a stream of frames by analyzing content differences between consecutive frames, comprising:(a) Identifying a content of each of a plurality of frames of a frames stream by applying a plurality of visual classification functions to each frame. The content comprises one or more of a plurality of visual elements.(b) Determining a content difference between every two consecutive frames of the plurality of frames by comparing the content of the two consecutive frames.(c) Detecting a scene change between the two consecutive frames when the content difference exceeds a pre-defined threshold. The scene change defines a separation between consecutive scenes of a plurality of scenes in the frames stream wherein each of the scenes comprises a subset of the plurality of frames.