Video Retrieval Using Split Query Embeddings for Scene Dynamics
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Solution Overview
Problem
Current techniques for retrieving relevant video footage from fleet vehicles consume excessive computing resources and fail to identify pertinent events due to their inability to capture scene dynamics, leading to inefficiencies in identifying poor driving behaviors and handling accidents.
Innovation Solution
A video system that processes video data using image and video encoder models to extract embeddings, splits natural language queries into appearance and dynamics subqueries, and generates similarity measures to retrieve relevant footage based on combined metrics, incorporating metadata for enhanced precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current techniques search through all video footage to retrieve relevant segments, then comprehensive search coverage is achieved, but excessive computing resources are consumed
Solution Approach 1:
The system performs preliminary action by generating embeddings for all video frames and storing them in advance. This preprocessing step converts raw video data into compact numerical representations that can be quickly searched later, avoiding the need to reprocess entire videos during query operations.
Solution Approach 2:
The system creates compressed copies of video content in the form of embeddings—numerical vectors that capture essential visual and dynamic features. These embedding copies serve as efficient proxies for the original video data, enabling fast similarity searches without processing the full video files.
2Device complexity
If current techniques focus only on appearance in videos, then simple processing is maintained, but dynamics of scenes are not captured
Solution Approach 1:
The system segments the analysis of video content into two distinct components: appearance features (visual characteristics of objects and scenes) and dynamics features (motion patterns and temporal changes). This segmentation allows each aspect to be processed independently and then combined, maintaining clarity while capturing comprehensive information.
Solution Approach 2:
The system creates a composite representation by combining appearance embeddings and dynamics embeddings into a unified video embedding. This composite structure integrates multiple types of information (visual appearance and motion dynamics) into a single searchable representation, preserving both aspects simultaneously.
3Measurement precision
If manual review of video footage is performed, then accurate identification of pertinent events is possible, but time consumption increases
Solution Approach 1:
The system introduces embeddings as an intermediary layer between raw video data and event identification. These embeddings serve as a bridge that translates visual content into a format suitable for efficient similarity-based searching, enabling automated retrieval that approaches manual review accuracy without the time cost.
Solution Approach 2:
The system replaces the mechanical process of manual video review with an automated computational system. By substituting human examination with algorithmic similarity searching based on embeddings, the system achieves comparable accuracy in identifying pertinent events while dramatically reducing the time required.
4Reliability
If all video data is stored and searched, then complete data availability is maintained, but storage optimization is reduced
Solution Approach 1:
The system creates compressed numerical copies (embeddings) of video content that occupy minimal storage space compared to the original video files. These embedding copies retain the essential information needed for searching and retrieval, serving as space-efficient proxies for the full video data.
Solution Approach 2:
The system transforms video data from its original high-dimensional pixel format into a compressed parameter-based representation (embeddings). This parameter transformation reduces the data dimensionality and storage requirements while preserving the semantic information necessary for accurate video retrieval.
Data Source
AI summary
A device may receive video data and description data, and may extract image embeddings and video embeddings from the video data. The device may receive a query that includes a natural language description of a scene to be retrieved from the video data. The device may split the query into a first subquery and a second subquery, and may process the first and second subqueries, with a model, to generate a first query embedding and a second query embedding. The device may generate appearance similarity measures based on the image embeddings and the first query embedding, and may generate dynamic similarity measures based on the video embeddings and the second query embedding. The device may combine the appearance similarity measures and the dynamics similarity measures into combined similarity measures, and may retrieve video segments based on the combined similarity measures.