Self-Supervised Video Shot Segmentation via Frame Anomaly Detection
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Solution Overview
Problem
Manual video shot segmentation is labor-intensive and prone to errors, requiring significant time and resources, especially with the need for annotated data in supervised learning approaches for video processing.
Innovation Solution
Implementing a self-supervised machine learning model for frame-anomaly based video shot segmentation, which creates a synthetic shot dataset, pre-trains, and fine-tunes the model to automatically segment video frames into shots without human intervention, reducing manual effort and error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual shot segmentation is used to annotate video data for supervised learning, then the quality and accuracy of training data is improved, but the time consumption and labor requirements increase significantly
Solution Approach 1:
The system uses self-supervised learning where the model automatically generates its own training labels by detecting frame anomalies and shot boundaries without human annotation. The model segments videos into shots autonomously, creating synthetic shot datasets that serve as training data, thereby eliminating the need for manual annotation while maintaining segmentation quality
Solution Approach 2:
The system creates synthetic shot datasets by copying and adapting existing video content into standardized shot formats. Instead of manually creating annotations, the system generates synthetic training data that mimics real shot structures, allowing the model to learn shot segmentation patterns without human intervention
2Measurement precision
If manual shot segmentation is performed to create training data, then the training data quality is improved, but the productivity and efficiency decrease
Solution Approach 1:
The system achieves both high training data quality and productivity through self-supervised learning. The model automatically generates synthetic shot datasets with precise shot boundary annotations by analyzing frame anomalies, eliminating manual labor while maintaining data quality. This autonomous process can process large volumes of video content efficiently
Solution Approach 2:
The system performs preliminary shot segmentation to create synthetic training datasets before actual model training. By pre-processing videos into standardized shot formats and generating synthetic annotations in advance, the system prepares high-quality training data automatically, improving both data quality and overall processing efficiency
3Measurement precision
If supervised learning approach is used with manually annotated data, then the model training accuracy is improved, but the complexity and resource requirements increase
Solution Approach 1:
The system replaces complex supervised learning pipelines with self-supervised learning. Instead of requiring manual annotation systems, data quality control mechanisms, and extensive human-in-the-loop processes, the model autonomously generates its own training data by detecting frame anomalies and shot boundaries, simplifying the overall system architecture while maintaining training accuracy
Solution Approach 2:
The system creates synthetic training data by copying and transforming existing video content into standardized shot formats with automatic annotations. This approach eliminates the need for complex manual annotation systems and data validation pipelines, reducing system complexity while providing sufficient training data quality for accurate model training
Data Source
AI summary
An electronic device and a method for implementation for frame-anomaly based video shot segmentation using self-supervised machine learning (ML) model is disclosed. The electronic device receives video data including a set of video frames and creates a synthetic shot dataset including a set of synthetic shots. The electronic device pre-trains an ML model and selects the training data including a first subset of video frames corresponding to a first synthetic shot. The electronic device fine-tunes the pre-trained ML model and selects a test video frame. The electronic device applies the fine-tuned ML model on the test video frame to determine whether the test video frame corresponds to an anomaly. The electronic device labels the first subset of video frames as a single shot. The set of video frames is segmented into a set of shots. The electronic device controls a rendering of the set of shots on a display device.