Entity Network Construction from Video Spatio-Temporal Correlations
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Solution Overview
Problem
Current systems lack the ability to effectively and automatically detect and relate entities from video data, deduce their relationships, and construct entity networks for analysis, especially in contexts involving clutter and uncertainty.
Innovation Solution
A computer-implemented method for deriving an attribute entity network from video data by extracting entities, tracking their trajectories, detecting spatio-temporal correlations, and constructing a graph with nodes representing entities and links representing associations, using Spatial-Temporal Object Analysis, hierarchical weighted graph matching, and Markov Logic Networks for reasoning and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic entity detection and relationship inference is implemented from video data, then the capability to detect and relate entities is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of entity relationship inference into distinct modules: entity detection module, trajectory tracking module, event detection module, and relationship inference module. Each module handles a specific aspect of the processing pipeline, making the overall system more manageable and maintainable despite the high level of automation.
Solution Approach 2:
The patent introduces intermediate data structures and representations, such as trajectory representations and event models, that serve as mediators between raw video data and final relationship inferences. These intermediaries simplify the complexity by providing structured intermediate results that can be processed by subsequent modules.
2Reliability
If entity networks are constructed from video data with clutter and uncertainty, then the ability to handle uncertain conditions is improved, but the difficulty of detecting and measuring entities increases
Solution Approach 1:
The system changes parameters such as detection thresholds, confidence levels, and tracking parameters dynamically based on scene complexity and uncertainty. This allows the system to adapt to varying conditions of clutter and uncertainty, improving reliability while managing the difficulty of entity detection in challenging environments.
Solution Approach 2:
The system implements feedback mechanisms where detection results are continuously refined based on trajectory information and contextual relationships. Uncertain detections can be re-evaluated using additional temporal and spatial context, improving the reliability of entity detection in cluttered environments.
3Measurement precision
If spatio-temporal motion correlation analysis is performed to detect events, then the accuracy of event detection is improved, but the loss of time for processing increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing trajectory representations and motion patterns during the tracking phase. This preliminary processing enables faster event detection later, as the computationally intensive trajectory analysis has already been performed, reducing the time loss during actual event detection.
Solution Approach 2:
The system applies partial action by focusing spatio-temporal correlation analysis only on selected entity pairs that show potential for interaction, rather than analyzing all possible pairs. This selective approach maintains high detection accuracy while significantly reducing the overall processing time.
Data Source
AI summary
A computer implemented method for deriving an attribute entity network (AEN) from video data is disclosed, comprising the steps of: extracting at least two entities from the video data; tracking the trajectories of the at least two entities to form at least two tracks; deriving at least one association between at least two entities by detecting at least one event involving the at least two entities, said detecting of at least one event being based on detecting at least one spatio-temporal motion correlation between the at least two entities; and constructing the AEN by creating a graph wherein the at least two objects form at least two nodes and the at least one association forms a link between the at least two nodes.