Multi-Modal Ensemble for Video Scene Change Detection
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Solution Overview
Problem
Current technologies face challenges in accurately and efficiently detecting scene changes in multimedia titles, such as videos, due to the lack of a clear definition and sufficient training datasets, which hinders the insertion of secondary content like advertisements without disrupting the viewer experience.
Innovation Solution
The implementation of a multi-modal ensemble of machine learning models, including visual and aural deep neural networks, to automatically detect scene changes by training on sparse labeled datasets and utilizing voice activity detection for improved performance and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional scene change detection methods are used, then computational resources are consumed, but detection accuracy is insufficient due to lack of clear definition and training datasets
Solution Approach 1:
The video input is divided into sequential frames that are processed individually by the neural network. Each frame is segmented and analyzed for scene change indicators, allowing the system to detect transitions frame-by-frame with high precision while maintaining reliability through consistent processing of each segment
Solution Approach 2:
Traditional rule-based scene change detection is replaced with a deep learning neural network that automatically learns scene change patterns from training data. This substitution of mechanical/rules-based systems with intelligent systems resolves the contradiction by providing both high accuracy through learned patterns and reliability through consistent neural network inference
2Ease of operation
If scene change detection is performed to enable secondary content insertion, then viewer experience can be enhanced, but computational resources required for processing increase
Solution Approach 1:
Scene change detection is performed in advance before secondary content insertion, creating a map of detected scene boundaries that can be stored and reused. This preliminary action enables efficient content insertion operations without requiring real-time computational resources during the actual insertion process
Solution Approach 2:
The neural network system automatically performs scene change detection and generates insertion points without requiring manual intervention or complex processing during content insertion. The system serves itself by having the detection phase prepare all necessary information, reducing computational load during the insertion phase
Data Source
AI summary
Techniques for automatic scene change detection are described. As one example, a computer-implemented method includes receiving a request to train an ensemble of machine learning models on a training dataset of videos having labels that indicate scene changes to detect a scene change in a video, partitioning each video file of the training dataset of videos into a plurality of shots, training the ensemble of machine learning models into a trained ensemble of machine learning models based at least in part on the plurality of shots of the training dataset of videos and the labels that indicate scene changes, receiving an inference request for an input video, partitioning the input video into a plurality of shots, generating, by the trained ensemble of machine learning models, an inference of one or more scene changes in the input video based at least in part on the plurality of shots of the input video, and transmitting the inference to a client application or to a storage location.