Object Tracker Feedback for Real-Time Detector Retraining
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Solution Overview
Problem
Object detectors, such as machine learning models, often fail to accurately detect objects in real-time or near real-time operations, particularly in resource-constrained environments, limiting their effectiveness in autonomous systems.
Innovation Solution
Utilizing an object tracker to generate tracking data that updates the object detector by associating object representations across frames, allowing for improved performance without significant additional training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If object detectors are used for real-time or near real-time operation in resource constrained implementations, then processing speed is improved, but detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary object tracking using an object tracker before applying the object detector. By pre-identifying candidate objects and their trajectories through tracking, the detector only needs to confirm and refine these pre-selected objects, significantly reducing computational complexity while maintaining high detection accuracy for relevant objects.
Solution Approach 2:
The system segments the object detection process into two distinct stages: (1) object tracking stage that identifies candidate objects and maintains their trajectories across frames, and (2) object detection stage that confirms and refines the tracked objects. This segmentation allows each component to be optimized independently, with the tracker handling broad surveillance and the detector focusing on precise identification.
2Measurement precision
If training data collection is increased to improve object detector performance, then detection accuracy is improved, but data collection time and resources increase
Solution Approach 1:
The system uses its own operational data during real-time tracking to continuously improve detector performance. By monitoring tracking results and detector outputs during normal operation, the system automatically identifies cases where the detector failed to detect tracked objects, collecting these failure cases as targeted training data without requiring separate data collection campaigns.
Solution Approach 2:
The system implements a feedback loop where tracking data and detector outputs are continuously monitored. When the detector fails to detect an object that the tracker has identified, this information is fed back as a training example to retrain and improve the detector. This closed-loop feedback mechanism enables continuous performance improvement using data generated during normal system operation.
Data Source
AI summary
In various examples, object tracker-based data generation can be performed for detector model training. For example, an object tracker can generate tracking data with respect to one or more objects tracked between a first image frame and a second image frame. The tracking data can be used to update an object detector that performs object detection on the image frames, such as where the object detector detects the one or more objects in the first image frame and not in the second image frame. The use of the tracking data to retrain or otherwise update the object detector can allow for the accuracy of the object detector to be increased without requiring significant training data resources.