Continuous Learning Object Tracking Drift Correction
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Solution Overview
Problem
Current object tracking in computer vision is limited by 'drift' issues, where the tracking position of a target object over time becomes offset due to error propagation, leading to inaccurate identification and eventual tracking failure, making long-term tracking challenging and typically effective for only short periods.
Innovation Solution
The implementation of a continuous learning framework that accumulates and updates samples in a database, using a discriminative correlation filter with factorized convolution operators to reduce parameters and limit error propagation, allowing for continuous refinement of the object tracking model without discarding valuable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If conventional object tracking is used, then tracking can be implemented, but drift occurs and tracking fails after short periods
Solution Approach 1:
The patent implements feedback by continuously accumulating training samples from successful tracking instances and using them to retrain the tracking model. This closed-loop feedback mechanism allows the system to learn from actual tracking performance and correct drift errors, enabling long-term reliable tracking without manual intervention.
Solution Approach 2:
The patent performs preliminary action by accumulating and storing training samples during successful tracking periods before drift occurs. These pre-accumulated samples are then used to retrain the model and reset tracking, preventing future drift errors and extending tracking duration.
2Adaptability or versatility
If tracking model parameters are continuously updated, then tracking adapts to appearance changes, but error propagation increases causing drift
Solution Approach 1:
The patent discards erroneous tracking results when drift is detected and recovers by retraining the model using only accurate training samples from successful tracking instances. This selective discarding and recovering mechanism prevents error propagation while maintaining adaptability to genuine appearance changes.
Solution Approach 2:
The patent changes parameters by selectively updating the tracking model only with high-quality training samples that confirm accurate tracking. This controlled parameter update strategy allows adaptation to appearance variations while preventing drift caused by erroneous updates.
3Measurement precision
If training samples are accumulated for continuous learning, then model accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies partial action by accumulating only the necessary training samples from successful tracking instances rather than processing all available data. This selective sampling approach maintains high tracking precision while reducing computational complexity and avoiding unnecessary processing.
Data Source
AI summary
A long-term object tracker employs a continuous learning framework to overcome drift in the tracking position of a tracked object. The continuous learning framework consists of a continuous learning module that accumulates samples of the tracked object to improve the accuracy of object tracking over extended periods of time. The continuous learning module can include a sample pre-processor to refine a location of a candidate object found during object tracking, and a cropper to crop a portion of a frame containing a tracked object as a sample and to insert the sample into a continuous learning database to support future tracking.