Scale-Predictive Target Tracking for Real-Time Accuracy
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Solution Overview
Problem
Existing object tracking technologies consume excessive computing resources and accumulate errors, affecting real-time tracking and accuracy, particularly in environments with varying scales.
Innovation Solution
A method and apparatus that utilize a scale feature-based approach, including multi-scale template region of interest (ROI) features and correlation calculations to predict and adjust tracking results, reducing computational load and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional object tracking methods are used, then tracking functionality is provided, but computing resource consumption is excessive and real-time performance deteriorates
Solution Approach 1:
The patent segments the tracking process into distinct modules: a tracker that performs initial tracking, a scale predictor that predicts target scale, a verifier that validates results, and a scale adjuster that refines bounding boxes. This segmentation allows each module to perform specialized functions efficiently, reducing overall computational overhead while maintaining real-time performance
Solution Approach 2:
The patent applies partial action by selectively performing verification and scale adjustment only when necessary. The verifier determines whether tracking has succeeded based on scale features, and the scale adjuster only adjusts results when scale drift is detected. This selective approach reduces computing resource consumption while maintaining tracking accuracy
2Measurement precision
If conventional object tracking methods are used, then tracking is performed, but cumulative scale errors accumulate and tracking accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the scale predictor continuously predicts target scale based on current tracking results, the verifier checks whether tracking has succeeded using scale features, and the scale adjuster adjusts bounding boxes when scale drift is detected. This closed-loop feedback system continuously corrects cumulative scale errors, maintaining tracking accuracy over extended durations
Solution Approach 2:
The scale predictor performs preliminary scale prediction before final tracking result determination. By predicting the expected scale of the target in advance, the system can prepare appropriate verification criteria and adjustment parameters, preventing error accumulation before it occurs
3Measurement precision
If multi-scale templates are used, then scale accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary extraction of scale features from the template image before tracking operations. The scale feature extractor identifies key scale-related characteristics once during template preparation, and these pre-extracted features are then reused during tracking without requiring repeated multi-scale template matching, significantly reducing computational complexity while maintaining scale prediction accuracy
Data Source
AI summary
A processor-implemented method with target tracking includes: generating a first target tracking result based on a search region of a current frame image; determining a scale feature of the first target tracking result; predicting a scale of a target in the search region based on the scale feature of the first target tracking result; and generating a second target tracking result by adjusting the first target tracking result based on a scale predicting result.