Security Video Search Using Natural Language and Shared Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Efficiently searching and identifying events in large volumes of video information captured by video cameras in monitored areas is difficult due to input limitations and the cumbersome nature of conventional methods.
Innovation Solution
A system that maps natural language inputs and video frames into a common multidimensional space using separate machine learning models, enabling accurate search and detection through similarity comparisons and improved ML model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If video cameras are installed throughout the monitored area to capture comprehensive video information, then the coverage and monitoring capability are improved, but the amount of video information increases making search and identification difficult and inefficient
Solution Approach 1:
The patent extracts key information from video frames by generating text descriptions that capture essential event characteristics. This extraction transforms the raw video data into a condensed textual representation that can be efficiently searched and identified, resolving the contradiction between comprehensive coverage and search efficiency.
Solution Approach 2:
The patent introduces text descriptions as an intermediary between video frames and user queries. This intermediary layer translates visual information into a format that can be easily searched using natural language, enabling efficient search operations without requiring direct analysis of the entire video stream.
2Ease of operation
If conventional search methods are used to search video information, then the system complexity remains low, but the input limitations and cumbersome nature make it difficult to identify events of interest
Solution Approach 1:
The patent replaces conventional mechanical search methods with machine learning-based text generation and similarity comparison. This substitution enables intuitive natural language queries while automating the complex tasks of video analysis and event identification, improving ease of operation without requiring users to manually navigate complex search interfaces.
3Measurement precision
If machine learning models are used to generate text descriptions from video frames, then the search accuracy and event identification capability are improved, but the computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by generating text descriptions only for sampled video frames rather than processing every frame continuously. This selective processing maintains search accuracy for event identification while reducing overall computational energy consumption compared to full-frame analysis.
Data Source
AI summary
A system may be configured to provide natural language search over security videos. In some aspects, the system may generate a first representation of sampled video information in a multidimensional format via a first machine learning model, and receive a request including a natural language input. Further, the system may generate a second representation of the natural language input in the multidimensional format via a second machine learning model that is a different from the first machine learning model, and determine that the first representation has a predefined relationship with the second representation. In addition, the system may present the second representation as a response to the request based on the first representation having the predefined relationship with the second representation.