Multi-Task Machine Learning Model for Real-Time Spatial Intelligence
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Solution Overview
Problem
Existing video analysis and surveillance systems fail to provide actionable intelligence and real-time responses to events of interest, as they perform discrete analysis tasks rather than comprehending scenes holistically.
Innovation Solution
A combined machine learning model core that processes video input in parallel to extract mutual information from distinct feature outputs, generating comprehensive and coherent interpretations of scenes, enabling real-time actionable intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If discrete detection and analysis methods are used for single computer vision tasks, then automated detections can be enabled, but comprehensive and actionable interpretations cannot be provided
Solution Approach 1:
The patent combines multiple discrete computer vision models (object detection, pose estimation, scene segmentation) into a unified multi-task learning framework that processes video data holistically, enabling both automated detection and comprehensive scene interpretation simultaneously through shared feature extraction and coordinated prediction heads
2Area of stationary object
If multiple video cameras provide multiple video feeds from different locations, then coverage of defined spaces is improved, but integration and coherent interpretation of events across feeds becomes complex
Solution Approach 1:
The patent implements a universal multi-task learning model that handles multiple computer vision tasks (detection, pose estimation, segmentation) and processes inputs from multiple camera feeds simultaneously, providing coherent event interpretation across the entire defined space through a single integrated system rather than separate specialized systems
3Productivity
If discrete analysis tasks are performed separately, then computational efficiency is maintained, but real-time comprehensive event detection and interpretation cannot be achieved
Solution Approach 1:
The patent segments the processing into efficient stages: shared feature extraction that processes multiple tasks simultaneously, followed by task-specific prediction heads that generate results in parallel, enabling real-time comprehensive event detection without sacrificing computational efficiency through the use of modern deep learning frameworks and optimized tensor operations
Data Source
AI summary
Systems and methods for augmenting real-time semantic information to a spatial rendering of a predefined space and providing a real-time situational awareness feed.