Monocular Bounding Box Scale Filtering for Non-Planar Skeleton Tracking
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Solution Overview
Problem
Existing methods struggle to accurately specify the region of a target object in an image containing multiple objects, particularly when the object moves in a non-planar space, such as in gymnastics, leading to inefficiencies and errors in skeleton recognition.
Innovation Solution
A multi-viewpoint image processing system that uses camera parameters and 3D coordinates to derive a bounding box scale range, filtering out non-target bounding boxes by setting threshold values based on statistical body shape data and camera geometry, enabling efficient and accurate extraction of the target object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustments are used to specify target object regions, then flexibility in handling different scenarios is improved, but processing efficiency and automation deteriorate
Solution Approach 1:
The system performs automatic bounding box filtering and target object specification without requiring manual intervention. The algorithm autonomously processes images, calculates scale ranges, and identifies target objects based on pre-stored reference data, eliminating the need for manual adjustments while maintaining high processing efficiency
2Measurement precision
If bounding boxes are filtered based on scale range, then target object identification accuracy is improved, but false exclusion of non-target objects increases
Solution Approach 1:
The system dynamically adjusts filtering parameters based on the specific scenario and reference data. By calculating scale ranges according to the actual image content and comparing them against pre-stored reference scale ranges for different object types, the system achieves accurate target identification while minimizing false exclusions through adaptive parameter adjustment
3Measurement precision
If 3D coordinate transformation is applied, then spatial accuracy in non-planar spaces is improved, but computational complexity increases
Solution Approach 1:
The system transforms 2D bounding box coordinates into 3D spatial coordinates by applying camera parameter transformations. This dimensional transition enables accurate representation of objects in non-planar spaces while the transformation itself is implemented through standardized mathematical operations that balance precision with computational efficiency
Data Source
AI summary
A method for automatically inferring a 2D scale range threshold to eliminate 2D object bounding boxes outside the target non-planar zone in a corresponding monocular image. The method leverages given camera parameters, 3D coordinates of the target non-planar zone vertices, and the real 3D scale of the target object. The process comprises three primary steps: 1) Deriving the real 3D object scale range from pre-acquired data, 2) Estimating the corresponding 2D bounding box scale range in the image using camera parameters, 3D coordinates of the target non-planar zone, and the real 3D scale range obtained in the first step, 3) Eliminating bounding boxes that fall outside the bounding box scale range.