Two-Stage Target Tracking with Depth Feature Segmentation
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Solution Overview
Problem
Current visual object tracking methods face challenges in accurately predicting the bounding box of a target in subsequent frames, especially in dynamic environments, due to interference from background features and resource constraints.
Innovation Solution
A two-stage target tracking method that involves obtaining depth features from target and search regions, generating global and local response diagrams to refine bounding box predictions, and classifying local feature blocks to suppress interference, thereby enhancing accuracy and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional visual object tracking methods are used to predict bounding boxes in subsequent frames, then tracking functionality is provided, but accuracy deteriorates due to background feature interference and resource constraints
Solution Approach 1:
The patent divides the tracking process into two distinct stages: a first stage that performs rough prediction to eliminate large errors, and a second stage that performs accurate prediction to refine the bounding box. This segmentation allows the system to balance computational resources while maintaining high accuracy, as each stage can be optimized independently for its specific purpose
Solution Approach 2:
The patent extracts depth features from images and uses them to generate response diagrams that separate target features from background interference. By extracting and processing depth information specifically, the system can focus computational resources on distinguishing the target from the background, improving accuracy without proportionally increasing overall resource consumption
2Measurement precision
If traditional tracking methods process entire images to predict bounding boxes, then comprehensive analysis is performed, but processing time increases reducing real-time performance
Solution Approach 1:
The patent segments the image processing into two stages: rough prediction that quickly eliminates large errors, and accurate prediction that refines the bounding box. This time-segmented approach ensures that the majority of processing time is spent on the more efficient first stage, while the second stage only processes the already-narrowed search space, maintaining real-time performance
Solution Approach 2:
The first stage performs preliminary action by conducting rough prediction to eliminate large errors and generate an initial bounding box estimate. This preliminary processing narrows down the search space before the second stage performs accurate prediction, significantly reducing the computational burden and processing time required for the final accurate result
3Reliability
If background features are included in the analysis, then comprehensive scene understanding is achieved, but interference from background features reduces tracking accuracy
Solution Approach 1:
The patent extracts depth features specifically from the image and uses them to generate response diagrams that highlight target features while suppressing background interference. By extracting and processing depth information separately from color and texture information, the system can reliably track targets even in complex backgrounds, as depth provides a differentiating dimension that background features lack
Solution Approach 2:
The patent applies different processing qualities to different parts of the image: the first stage performs rough prediction with lower computational quality to establish a baseline, while the second stage performs accurate prediction with higher computational quality on the refined search space. This local quality differentiation ensures tracking stability while maintaining accuracy where it matters most
Data Source
AI summary
Disclosed is a target tracking method and apparatus. The target tracking apparatus includes a processor configured to obtain a first depth feature from a target region image and obtain a second depth feature from a search region image, obtain a global response diagram between the first depth feature and the second depth feature, acquire temporary bounding box information based on the global response diagram, updated the second depth feature based on the temporary bounding box information, obtain local feature blocks based on the first depth feature, obtain a local response diagram based on the local feature blocks and the updated second depth feature, and determine output bounding box information based on the local response diagram.