Detection Frame Position Correction Using Past and Future Frames
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Solution Overview
Problem
Existing detection frame position accuracy improvement systems are limited to cases where the previous frame accuracy is high, and do not effectively utilize information from frames before and after the target frame for correction.
Innovation Solution
A system and method that utilizes a time-series image input unit, object detection, detection frame position distribution estimation, prediction, uncertainty estimation, and correction unit to improve detection frame position accuracy by leveraging information from frames before and after the target frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detection frame position is estimated using only the previous frame, then the estimation process is simple, but the detection frame position accuracy is limited
Solution Approach 1:
The system performs preliminary actions by estimating detection frame positions using both previous and future frames before final correction. The prediction unit uses future frame information to anticipate position drift, and the correction unit applies this prediction to improve current frame accuracy, effectively preparing correction data in advance
Solution Approach 2:
The system maintains continuous useful action by utilizing detection results from multiple consecutive frames (past and future) rather than isolated frames. This continuous utilization of temporal information from the time-series data enables sustained high accuracy across the video sequence, preventing accuracy degradation over time
2Measurement precision
If only previous frame information is used for detection frame position estimation, then processing time is reduced, but accuracy improvement is limited to cases where previous frame accuracy is high
Solution Approach 1:
The system performs preliminary estimation using future frame information to predict current frame position drift before final correction is applied. This allows the correction unit to compensate for accumulated errors proactively rather than reactively, improving accuracy without requiring extensive post-processing time
Solution Approach 2:
The system implements feedback by using detection results from future frames to correct current frame positions. The prediction unit continuously monitors detection accuracy trends and feeds this information back to the correction unit, which adjusts detection frame positions based on observed drift patterns, creating a closed-loop accuracy improvement system
Data Source
AI summary
Provided are a detection frame position accuracy improvement system and a detection frame position correction method capable of estimating a detection frame position with high accuracy using information before and after a target frame. The detection frame position accuracy improvement system includes a time-series image input unit 10 that inputs time-series images, an object detection unit 20 that detects a target object with the time-series images, a detection frame position distribution estimation unit 30 that estimates a distribution of detection frame position coordinates at time t from detection results of the target object up to time t−1 (t is a positive integer), a detection frame prediction unit 40 that predicts positions of a detection frame at times t+1 to t+n (n is a positive integer) according to the detection results and the distribution, a detection frame uncertainty estimation unit 50 that updates the distribution of the detection frame position coordinates at time t according to degrees of overlap between the detection results of the target object at the times t+1 to t+n and the predicted detection frame and estimates uncertainty of a detection frame at time t, and a detection frame correction unit 60 that corrects the detection frame at time t on the basis of the detection frame and the uncertainty.