Hierarchical Object Tracking for Real-Time Behavior Inference
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Solution Overview
Problem
Existing computer vision systems for object detection, tracking, and event generation are computationally intensive and require human operators, failing to accurately monitor and infer human behavior in real-time while ensuring privacy.
Innovation Solution
A computer vision process that creates and links track records for detected objects, utilizing hierarchical organization and metadata to infer information about objects, and generates events based on object behavior, without the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion segmentation and machine learning techniques are used for object detection and tracking, then object detection capability is improved, but computational intensity increases
Solution Approach 1:
The patent segments the object detection task into multiple stages: initial detection using simple motion segmentation, followed by selective application of more complex machine learning techniques only to regions of interest. This hierarchical segmentation reduces overall computational intensity while maintaining detection accuracy.
Solution Approach 2:
The system applies full machine learning processing only to detected objects of interest rather than processing entire video frames with equal computational intensity. This partial action approach maintains high detection accuracy for target objects while significantly reducing overall computational load.
2Measurement precision
If machine learning techniques with large training datasets are used, then object analysis accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complexity by separating the training phase from the operational phase. Large datasets are used offline to train compact models, which are then deployed as simplified inference engines during operation. This reduces real-time system complexity while preserving accuracy.
Solution Approach 2:
The patent performs the computationally intensive model training and data processing in advance, before the actual object detection and tracking operations. This preliminary action prepares optimized models and structures that reduce complexity during real-time operation while maintaining high accuracy.
3Measurement precision
If real-time monitoring and behavior inference are implemented, then monitoring accuracy is improved, but computational requirements increase
Solution Approach 1:
The system segments behavior analysis into discrete events and patterns, processing only relevant behavioral changes in real-time rather than continuously analyzing all object properties. This selective processing maintains inference accuracy while improving processing efficiency.
Solution Approach 2:
The patent implements periodic sampling and event-triggered processing for behavior inference, updating analysis only when significant changes occur rather than continuous processing. This periodic approach maintains accurate real-time monitoring while reducing computational requirements during stable periods.
Data Source
AI summary
A computer vision process in which (i) a track record for one or more detected objects is created, the track record defining metadata or parameters for that detected object; and (ii) that track record is then linked to one or more higher-level track records for one or more higher-level objects.