Object Tracking Using Homography Matrix Correction
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Solution Overview
Problem
Existing object tracking systems fail to accurately correct object positions due to camera motion, particularly when using deep learning methods that struggle with generating datasets and excluding dynamic object regions during homography derivation.
Innovation Solution
An object tracking apparatus and method that uses RGB images to derive a homography matrix through feature point matching or unsupervised learning-based homography derivation schemes, correcting object positions and tracking changes by filtering out dynamic object features and using verification conditions to select the most accurate homography matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning scheme is used to obtain homography, then tracking accuracy can be improved, but it is difficult to generate a dataset and requires ground truth homography between two images
Solution Approach 1:
The system performs unsupervised learning to automatically derive homography matrices from image sequences without requiring manually annotated ground truth data. The algorithm self-adjusts by comparing predicted object positions with actual detected positions across frames, enabling the system to improve tracking accuracy autonomously without external dataset generation
Solution Approach 2:
A homography derivation network is introduced as an intermediary component that automatically learns the transformation between frames. This network acts as a mediator that bridges the gap between raw image data and accurate object tracking, eliminating the need for complex dataset preparation while maintaining high tracking precision
2Device complexity
If feature point matching scheme is used to obtain homography, then system complexity is reduced, but dynamic object regions are excluded or not considered
Solution Approach 1:
The system segments the image into static background regions and dynamic object regions, then applies different processing strategies to each. Feature point matching is performed on static regions to obtain initial homography, while dynamic object regions are separately tracked using the derived homography to correct their positions accurately
Solution Approach 2:
The system dynamically adjusts the homography derivation process by iteratively identifying and excluding dynamic object regions from the feature matching process. This allows the system to maintain simplicity while progressively improving accuracy by adapting to the dynamic nature of objects in the scene
3Productivity
If homography matrix is derived without verifying, then processing speed is improved, but tracking accuracy decreases due to incorrect homography
Solution Approach 1:
The system applies a verification process to only the critical parameters of the homography matrix rather than exhaustive verification of all possible transformations. This partial verification approach checks key geometric constraints and consistency conditions, providing sufficient accuracy improvement without the full computational cost of complete verification
Solution Approach 2:
The system performs preliminary verification checks on the homography matrix before applying it to object tracking. By pre-validating the homography parameters against geometric constraints and consistency conditions, the system prevents incorrect transformations from degrading tracking accuracy while maintaining efficient processing
Data Source
AI summary
An embodiment object tracking method includes receiving a first image at time t from a camera, deriving a homography matrix based on the first image and a second image at time t−1, correcting a position of a first object detected from the second image using the homography matrix, detecting a second object matched with the first object from the first image, and tracking a change in position of the second object based on the corrected position of the first object.