Monocular Ball Trajectory Reconstruction for Mobile 3D Analytics
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Solution Overview
Problem
Existing real-time sports analytic systems require multiple high-definition cameras and high-end hardware for accurate 3D trajectory reconstruction of ball objects, making them complex and expensive, preventing widespread adoption on low-cost, general-purpose mobile devices.
Innovation Solution
A method and system for reconstructing 3D ball trajectories using a single mobile device with AI-based computer vision techniques, analyzing monocular video to generate 2D trajectories and estimating depth information for real-time analytics on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple high-definition cameras and high-end hardware are used for accurate 3D trajectory reconstruction, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces the mechanical/optical system of multiple physical cameras with a computational system using AI-based computer vision on a single mobile device. The mechanical complexity of multiple camera arrays is substituted with software-based depth estimation algorithms that process monocular video data to reconstruct 3D trajectories.
Solution Approach 2:
The patent creates a computational model (3D trajectory reconstruction) that replicates the functionality of multiple-camera systems using a single camera's monocular video feed. The system generates virtual depth information through AI algorithms, effectively copying the 3D measurement capability without requiring physical multiple-camera hardware.
2Measurement precision
If multiple high-definition cameras and high-end hardware are used for accurate 3D trajectory reconstruction, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent employs inexpensive mobile devices with standard cameras instead of expensive specialized multi-camera systems. The solution uses off-the-shelf hardware that is widely available and affordable, making the technology accessible for mass adoption in sports analytics applications.
Solution Approach 2:
The patent substitutes expensive optical hardware with computational algorithms. By using AI-based depth estimation on monocular video, the system eliminates the need for costly multiple-camera equipment while maintaining acceptable measurement precision for sports analytics.
3Measurement precision
If dedicated sensors and complex triangulation techniques are used for 3D trajectory reconstruction, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces dedicated depth sensors and complex triangulation hardware with AI-based computational methods. The system uses neural networks and machine learning algorithms to estimate depth from monocular video, substituting specialized sensors with general-purpose mobile device cameras and software processing.
Solution Approach 2:
The system uses the mobile device's own camera and processing capabilities to generate depth information without requiring external dedicated sensors. The AI algorithms process the video data locally on the device, making the system self-sufficient and eliminating the need for additional specialized hardware components.
Data Source
AI summary
Methods and systems for object trajectory reconstruction are disclosed. The methods and systems perform steps of first, receiving a first trajectory in a first image space and a user's location. Next, generating at least one trajectory line based on the user's location and the first trajectory, where the trajectory line can include at least one projection point representing a projection of the object's position. And finally, for the trajectory line, determining an associated second trajectory by determining, for the projection point in the trajectory line, a distance between the object and a plane. Some embodiments of the present invention enable a resource-limited mobile device, such as a smartphone, to efficiently perform this method. Also disclosed are benefits of the new methods, and alternative embodiments of implementation.