Digital Video Tagging via Neural Feature Vectors
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Solution Overview
Problem
Conventional digital video tagging systems are inefficient and inaccurate in generating tags for digital videos, particularly in identifying and tagging actions, objects, and attributes across multiple frames, leading to difficulties in searching and organizing large collections of videos.
Innovation Solution
A digital video tagging system that uses machine learning to automatically identify actions, objects, and attributes by generating tagged feature vectors from frames of videos, utilizing neural networks to associate metadata with feature vectors and aggregate tags for efficient searching and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital video tagging systems are used, then video tagging can be performed, but the tagging accuracy and efficiency are insufficient
Solution Approach 1:
The video is divided into multiple frames, and each frame is processed independently to generate feature vectors. This segmentation allows the system to capture temporal dynamics and action information from individual frames while maintaining overall video context through aggregation of frame-level features.
Solution Approach 2:
A neural network serves as an intermediary that transforms raw video frame data into meaningful feature vectors. The neural network processes visual information from multiple frames and generates tagged feature vectors that represent actions, objects, and attributes, bridging the gap between raw video data and searchable tags.
2Reliability
If manual video searching is performed, then users can find specific videos, but it requires viewing the entire video which is time-consuming
Solution Approach 1:
The system performs preliminary tagging of videos by analyzing frames, generating feature vectors, and creating action tags before the user needs to search. This advance processing creates an indexed database of video content that enables rapid retrieval without requiring users to watch entire videos.
Solution Approach 2:
The patent replaces manual video viewing and searching with an automated computer vision system that uses neural networks to analyze video content and generate searchable tags. This substitution of mechanical human search with automated algorithmic processing dramatically reduces search time while maintaining retrieval accuracy.
3Measurement precision
If simple tagging methods are used, then processing is fast, but the tags cannot accurately describe actions and attributes
Solution Approach 1:
The system transitions from simple frame-level analysis to a multi-dimensional approach by generating feature vectors that capture spatial, temporal, and semantic dimensions of video content. The tagged feature vectors incorporate information from multiple frames and dimensions, enabling accurate action and attribute description while managing complexity through structured processing.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.