Video Cut Detection Using Neural Network Probability
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Solution Overview
Problem
Conventional video cut detection systems are inaccurate and inefficient, often failing to detect video cuts or producing false positives, particularly in videos with varying content, and require user interaction to set thresholds, limiting their suitability for automated workflows and multi-frame cuts like fades and wipes.
Innovation Solution
A video cut detection system using a neural network to generate feature vectors for digital video frames, determining the probability of video cuts within a sliding time window, and comparing it to a cut threshold, with automated training sequences to update the neural network coefficients, enabling precise detection of video cuts without user intervention and handling various types of cuts, including multi-frame ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional video cut detection systems use color histograms with fixed thresholds, then the system is simple to implement, but the detection accuracy deteriorates due to content dependence
Solution Approach 1:
The patent transforms the fixed threshold parameter into a dynamic probability threshold learned by a neural network. Instead of using a static threshold value that works for some content but not others, the system learns optimal detection parameters (probabilities) through training on diverse video data, allowing accurate detection across varying video content types.
Solution Approach 2:
The patent replaces the mechanical histogram-based thresholding system with a neural network-based probabilistic system. This substitution enables the system to automatically adapt to different video content characteristics without manual threshold adjustment, resolving the contradiction between simplicity and accuracy.
2Measurement precision
If conventional systems require user interaction to set threshold values, then detection accuracy can be improved for specific content, but productivity deteriorates due to manual intervention requirements
Solution Approach 1:
The patent implements self-service by training the neural network to automatically learn optimal detection parameters from training data. The system serves itself by adapting to different video content types without requiring user interaction for threshold setting, thereby maintaining high accuracy while achieving full automation for production workflows.
3Device complexity
If conventional systems use fixed time windows for detection, then the detection process is simple, but measurement precision deteriorates for multi-frame cuts
Solution Approach 1:
The patent applies dynamics by making the detection window adaptive rather than fixed. The neural network learns to adjust the effective detection window size and position based on the characteristics of each video segment, enabling precise detection of both hard cuts and multi-frame transitions like fades and wipes while maintaining computational efficiency.
4Device complexity
If conventional systems detect only hard cuts, then the detection algorithm remains simple, but adaptability deteriorates for multi-frame cuts
Solution Approach 1:
The patent achieves universality by designing a neural network that handles multiple cut types (hard cuts, fades, wipes, and other multi-frame transitions) through a single unified detection framework. This multi-functional approach eliminates the need for separate detection algorithms for different cut types while maintaining algorithmic simplicity through the network's learned features.
Data Source
AI summary
In implementations of determining video cuts in video clips, a video cut detection system can receive a video clip that includes a sequence of digital video frames that depict one or more scenes. The video cut detection system can determine scene characteristics for the digital video frames. The video cut detection system can determine, from the scene characteristics, a probability of a video cut between two adjacent digital video frames having a boundary between the two adjacent digital video frames that is centered in the sequence of digital video frames. The video cut detection system can then compare the probability of the video cut to a cut threshold to determine whether the video cut exists between the two adjacent digital video frames.