3D CNN Advertisement Detection via Spatial-Temporal Feature Segmentation
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Solution Overview
Problem
Accurate and automated detection of advertisements in video streams is crucial for TV analytics and measurement, but existing methods lack efficient solutions for classification, especially in handling diverse video attributes and temporal contexts.
Innovation Solution
A method utilizing a curated dataset to train both 2D and 3D convolutional neural networks (CNNs) to classify video content as advertisements or non-advertisements, where 2D CNNs learn visual and spatial features and 3D CNNs capture temporal relationships, with audio features aggregation improving detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D convolutional neural networks are used for advertisement detection in video streams, then classification accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The video stream is divided into discrete frames that are processed independently by the 2D CNN for spatial features, while temporal relationships are captured separately by the 3D CNN. This segmentation allows the system to handle complex 3D temporal-spatial analysis by breaking it into manageable 2D spatial processing and 1D temporal processing components, reducing overall computational complexity while maintaining high classification accuracy
Solution Approach 2:
The patent transitions from traditional 2D image processing to 3D volume processing by adding the temporal dimension. The 3D CNN processes video clips as volumetric data (height, width, time), enabling the network to capture temporal dynamics and motion patterns that 2D CNNs cannot detect, thereby improving advertisement detection accuracy without requiring manual feature engineering
2Loss of information
If 3D convolutional neural networks are used for advertisement detection, then temporal context understanding is improved, but processing speed decreases
Solution Approach 1:
The patent merges the outputs of two separate networks: a 2D CNN that extracts spatial features from individual frames and a 3D CNN that captures temporal relationships across frames. By combining these complementary feature representations, the system achieves comprehensive understanding of both spatial content and temporal context, improving detection accuracy while the modular architecture allows for efficient parallel processing
Solution Approach 2:
The 2D CNN performs preliminary spatial feature extraction from individual frames before the 3D CNN processes temporal relationships. This preliminary action reduces the dimensionality and complexity of the data that the 3D CNN must process, thereby improving processing speed while ensuring that spatial information is fully utilized before temporal analysis begins
Data Source
AI summary
A method is provided to classify whether video content is likely to be an advertisement or a non-advertisement. A curated database of video content items that includes a plurality of different video content items that were each previously identified as being an advertisement, and a plurality of different video content items that were each previously identified as not being an advertisement, are used to train a 2D CNN and a 3D CNN. The training of the 2D CNN includes learning characteristic visual and spatial features of advertisement images in the video content items compared to non-advertisement images in the video content items, the training resulting in weights being defined for the 2D CNN. The training of the 3D CNN includes learning a temporal structure and relationship over multiple image frames of the advertisements in the video content items compared to non-advertisement image frames in the video content items, the training resulting in weights being defined for the 3D CNN. The trained 2D CNN and 3D CNN are then used to determine the probability that newly identified video content should be classified as an advertisement.