Geo-Rectified Entity Tracking with AI Detection Validation
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Solution Overview
Problem
Existing AI-based automated entity detection systems often fail due to obstruction, misidentification, and discontinuities in image frames, leading to false positives and negatives in tracking entities within environments.
Innovation Solution
A system and method for validating or rejecting automated detections by allowing user input to correct AI-generated tracklets, incorporating user-provided detections, and geo-rectifying the track to align with static map data, thereby improving the accuracy of entity tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If AI-based automated entity detection is used to track entities, then detection speed and automation are improved, but detection accuracy and reliability deteriorate due to obstructions, misidentifications, and false positives/negatives
Solution Approach 1:
The system implements feedback by allowing users to validate or reject AI-generated detections. User feedback on detected entities is collected and used to correct inaccuracies, creating a closed-loop system that improves detection reliability while maintaining automation. This resolves the contradiction by enabling the automated system to learn from and correct its own errors through user interaction.
Solution Approach 2:
The system introduces an intermediary layer between automated detection and final tracking results. This intermediary consists of user validation and geo-rectification processes that mediate between AI detections and the final track output. The intermediary filters and corrects AI errors without completely removing automation, thus improving reliability while preserving the benefits of automated detection.
2Measurement precision
If user validation of each detection is implemented, then detection accuracy is improved, but processing time and operational complexity increase
Solution Approach 1:
The system applies partial validation by allowing users to validate or reject detections selectively rather than requiring complete manual review of all detections. This partial action approach maintains high accuracy for critical detections while reducing processing time by not uniformly applying validation to every single detection, thus resolving the time-accuracy tradeoff.
Solution Approach 2:
The system performs preliminary geo-rectification and track generation using AI detections before user validation. This preliminary action creates a draft track that can be quickly generated and then refined through selective user validation, reducing the overall time investment required while maintaining accuracy.
3Device complexity
If AI detection algorithms are used without correction, then system simplicity is maintained, but track accuracy deteriorates due to false positives and negatives
Solution Approach 1:
The system implements self-service by enabling users to directly validate and correct detections in the interface. Users can independently verify and adjust track accuracy without requiring complex automated correction algorithms. This self-service approach maintains relative system simplicity while significantly improving track accuracy through direct user intervention.
4Measurement precision
If geo-rectification is performed to align track with map data, then spatial accuracy is improved, but processing complexity and time increase
Solution Approach 1:
The system replaces complex mechanical or algorithmic geo-rectification processes with a simplified approach that leverages the user's visual verification and manual validation. Instead of implementing sophisticated automated coordinate transformation systems, the patent uses user feedback to implicitly perform geo-rectification, reducing processing complexity while maintaining spatial accuracy.
Data Source
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AI summary
Described herein are systems, methods, and non-transitory computer readable media for validating or rejecting automated detections of an entity being tracked within an environment in order to generate a track representative of a travel path of the entity within the environment. The automated detections of the entity may be generated by an artificial intelligence (AI) algorithm. The track may represent a travel path of the tracked entity across a set of image frames. The track may contain one or more tracklets, where each tracklet includes a set of validated detections of the entity across a subset of the set of image frames and excludes any rejected detections of the entity. Each tracklet may also contain one or more user-provided detections in scenarios in which the tracked entity is observed or otherwise known to be present in an image frame but automated detection of the entity did not occur.