Spatiotemporal Graph Video Analysis for Event Detection
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Solution Overview
Problem
Current video storage solutions face challenges in balancing transfer speed and capacity, leading to inefficient analysis of full-motion video data, where human analysts struggle to detect important events due to fatigue and information overload, resulting in missed opportunities.
Innovation Solution
The implementation of a spatiotemporal graph representation-based method for activity detection in videos, utilizing artificial neural networks to identify and analyze spatiotemporal relationships between entities in video frames, enabling efficient storage, retrieval, and automatic summarization of video data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full-motion video data is stored and analyzed manually, then complete video information is preserved, but analysis time and cost increase significantly
Solution Approach 1:
The system performs preliminary processing of video data by extracting key frames and building spatiotemporal graphs in advance, organizing entities and their relationships into structured representations before detailed analysis is needed. This preliminary structuring enables rapid querying and analysis without requiring manual review of complete video footage.
Solution Approach 2:
The patent introduces an intermediary representation layer (spatiotemporal graphs) between the raw video data and the analysis process. This intermediary structure captures essential entities, attributes, and relationships, allowing analysts to query and analyze video content without directly examining all video frames, thus reducing analysis time while preserving information integrity.
2Measurement precision
If human analysts review video footage manually, then detailed inspection is possible, but fatigue and information overload cause important events to be missed
Solution Approach 1:
The system segments video analysis into distinct computational tasks: key frame extraction, entity detection, relationship identification, and activity recognition. By dividing the complex analysis process into manageable segments handled by automated algorithms, the system maintains high detection accuracy without subjecting human analysts to information overload, thereby improving reliability.
Solution Approach 2:
The patent replaces the mechanical process of manual video review with automated computer vision and machine learning systems. These systems detect entities, track movements, and identify activities without fatigue, eliminating the reliability issues associated with human analysts while maintaining or improving detection precision through consistent algorithmic application.
3Loss of information
If complete video data is stored for later analysis, then all information is available for retrieval, but storage capacity requirements increase
Solution Approach 1:
The system extracts essential information from complete video data by identifying key frames that contain critical events and entities. Instead of storing and analyzing all video frames, the system extracts and stores only the necessary key frames and their associated spatiotemporal relationships, significantly reducing storage requirements while maintaining information availability for analysis.
Solution Approach 2:
The system performs preliminary processing to identify and extract key frames and build spatiotemporal graphs before storage. This preliminary action filters out redundant data and retains only essential information, allowing the system to store compressed representations that maintain full analytical capability while reducing storage capacity requirements.
Data Source
AI summary
Methods, systems, and apparatuses, among other things, may detect and store activity in videos based on a spatiotemporal graph representation. Spatiotemporal proximity graphs may be built based on one or more received tracks and may include one or more nodes and each node may include one or more attributes associated with a corresponding entity. One or more spatiotemporal relationships may be identified between the entities based on each spatiotemporal proximity graph one or more activities of the entities may be identified based on the spatiotemporal relationships.