Multimodal Video Tagging for Complex Action Search
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Solution Overview
Problem
Existing video tagging systems struggle to analyze complex actions in recent videos, particularly those focused on actions rather than specific persons or objects, and lack efficient methods for users to initiate searches directly from video content using external search engines.
Innovation Solution
A device and method for multimodal video analysis that determines video-level and time-stamped tags using frames, audio information, and metadata, employing techniques like ASR, OCR, and NLP, enabling efficient tagging and recommendation generation for user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If machine learning is used to automatically generate video tags for indexing purposes, then video content understanding is improved, but the ability to initiate external searches directly from video content remains insufficient
Solution Approach 1:
The video tagging system is enhanced to serve dual purposes: traditional indexing/search suggestion and direct external search initiation. By making the tagging system universal, it can function both as a content understanding tool and as a search launcher, allowing users to click on tags to initiate searches in external search engines, thereby resolving the contradiction between information understanding and operational ease.
Solution Approach 2:
The generated video tags act as an intermediary element between video content and external search engines. Instead of requiring users to manually copy content or navigate through the platform, the tags serve as clickable mediators that directly initiate external searches, bridging the gap between video understanding and search initiation functionality.
2Measurement precision
If users manually copy and paste tag content into external search engines, then search accuracy is improved, but user effort and time consumption increase significantly
Solution Approach 1:
Instead of requiring users to manually copy tag text, the system generates clickable tag elements that automatically copy the relevant search term and initiate the external search with a single click. This eliminates the manual copying step while preserving search accuracy, as the same tag content is used but delivered through an automated clickable interface.
Solution Approach 2:
The system performs the search initiation action in advance by preparing clickable tag elements that are already formatted and ready to launch external searches. When users click these pre-prepared tags, the search is immediately initiated with the correct query, eliminating the need for users to manually type or copy search terms, thus reducing time and effort while maintaining precision.
3Measurement precision
If traditional video tagging systems focus on specific persons or objects, then tagging accuracy is improved, but the ability to analyze complex actions in recent videos deteriorates
Solution Approach 1:
The tagging system transitions from static object/person identification to dynamic action recognition. By implementing action detection capabilities that analyze motion patterns and temporal sequences in video frames, the system can accurately tag complex actions while maintaining adaptability to various video content types, resolving the contradiction between precision and versatility.
Solution Approach 2:
The system adds a temporal dimension to traditional spatial tagging by analyzing action sequences over time. Instead of only identifying objects at specific moments, the system examines motion patterns and temporal relationships across multiple frames, enabling accurate tagging of complex actions while preserving the ability to identify persons and objects, thus achieving both precision and adaptability.
Data Source
AI summary
A device is configured to receive a video stream. The device is further configured to determine video-level tags and time-stamped tags, based on at least two frames of the video stream, audio information of the video stream and an inference technique.