Context-Aware Video Search Using AI Scene Analysis
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Solution Overview
Problem
Existing multimedia devices are unable to search for content associated with the audio/video currently being streamed in response to a user's search query, treating general search queries as operations outside the context of the main audio/video operation and failing to determine if a search query is related to the currently playing content.
Innovation Solution
Implementing an AI engine that receives a search query from a user, determines if it is related to a video stream, analyzes the video stream to derive contextual data, and searches a database for relevant information based on this data, allowing for context-aware searching within video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing multimedia devices treat general search queries as operations outside the context of main audio/video operation, then search functionality is provided, but the devices are unable to determine that a user has initiated a search query regarding the audio/video currently being played
Solution Approach 1:
The system pre-processes video streams by extracting frames and generating contextual data (object detection, scene analysis, metadata extraction) before search queries are initiated. This preliminary action enables the AI engine to immediately analyze relevant context when a user searches, rather than processing everything in real-time during playback.
Solution Approach 2:
An AI engine acts as an intermediary layer between the video streaming system and the search functionality. This intermediary receives video streams, extracts contextual information, and uses this context to enhance search queries, bridging the gap between generic search operations and context-aware results.
2Loss of information
If an AI engine analyzes video streams to derive contextual data for search queries, then search relevance is improved, but processing complexity and computational resources increase
Solution Approach 1:
The video stream processing is segmented into discrete frames, with contextual data extracted from individual frames rather than analyzing the entire continuous video stream. This segmentation reduces computational complexity by breaking down the large-scale problem into manageable smaller units that can be processed independently.
Solution Approach 2:
The system extracts only the essential contextual data needed for search functionality (object detection, scene type, key metadata) rather than performing complete video analysis. This partial action approach provides sufficient context for improved search relevance without the full computational overhead of comprehensive video processing.
3Measurement precision
If contextual data is derived from video frames for search queries, then search accuracy is improved, but the time required to process and return search results increases
Solution Approach 1:
Contextual data from video frames is pre-extracted and stored alongside the video metadata before search operations are performed. This preliminary extraction of objects, scenes, and key elements enables rapid search execution by eliminating the need to analyze video content in real-time when a query is submitted.
Solution Approach 2:
The system focuses contextual analysis on specific local regions or elements within video frames that are most relevant for search (such as detected objects, text overlays, or scene boundaries) rather than uniformly processing the entire frame. This selective local analysis improves search accuracy while reducing overall processing time.
Data Source
AI summary
Novel tools and techniques are provided for scene aware searching. A system may include a media player configured to play a video stream, a database, and a server configured to host an artificial intelligence (AI) engine. The server may further include a processor and a non-transitory computer readable medium comprising a set of instructions that, when executable by the processor to receive, from the media device, a search query from a user. The AI engine may further be configured to obtain the video stream associated with the search query, identify one or more objects in the video stream, derive contextual data associated with the one or more objects, identify one or more matches based on the contextual data, and determine a result of the search query.