Object Tracking Correction Through Recently Lost Object Matching
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Solution Overview
Problem
Current object tracking methods in images suffer from anomalies such as multiple tracklets corresponding to the same object, swapped identifiers, and incorrect assignment of identifiers to non-existent objects like shadows or reflections, leading to inefficiencies and errors.
Innovation Solution
A method that assigns a new identifier to newly detected objects and compares them to recently lost objects within a threshold period to confirm their identity, using appearance and position comparisons, with optional operator validation to correct tracking errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a new identifier is assigned to every newly detected object, then the tracking system can identify and follow multiple objects, but it creates multiple tracklets for the same object when the object is temporarily obscured or leaves the field of view
Solution Approach 1:
The system performs preliminary comparison between newly detected objects and recently lost objects before finalizing identifier assignment. By checking if a new object matches any recently lost object within a threshold period, the system proactively prevents the creation of duplicate tracklets, ensuring object identity continuity while maintaining tracking accuracy.
2Measurement precision
If the system continuously monitors and compares objects to maintain accurate tracking, then tracking precision improves, but computational complexity and processing time increase
Solution Approach 1:
The system applies comparison operations selectively rather than universally. It only compares newly detected objects with recently lost objects within a specific time threshold, rather than comparing all objects with all other objects. This localized approach maintains high identification accuracy while significantly reducing computational complexity.
3Productivity
If the tracking system assigns identifiers based solely on current detection, then processing speed is maintained, but errors occur when objects are temporarily obscured or when shadows/reflections are detected
Solution Approach 1:
The system incorporates feedback by comparing newly detected objects against the history of recently lost objects. This feedback mechanism allows the system to verify whether a newly detected object is actually a return of a previously lost object or a genuine new object, thereby reducing detection errors while maintaining processing efficiency through targeted comparisons.
Data Source
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AI summary
The invention relates to a method (100) for correcting the tracking of object(s) in images, said tracking comprising an allocation of an identifier to each new object used to constitute a tracklet associated with said object and retained as long as said object has not been lost during said tracking; said correction method (100) comprising, for at least one new object detected on a current image, a step (104) of comparing said new object to at least one recently lost object: - which has been tracked, and - whose tracking has been lost for a period less than a predetermined threshold period before the capture of said current image; to confirm or not that it is a new object. It also relates to a computer program and a device implementing such a method, as well as a method and a system for tracking objects implementing such an object tracking correction.