Monocular Ball Trajectory Reconstruction for Mobile 3D Sports Tracking
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Solution Overview
Problem
Existing real-time sports analytic systems require complex setups with multiple high-definition cameras and high-end hardware, making them costly and impractical for widespread use on mobile devices, especially for accurately tracking 3D ball trajectories in ball sports.
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 derive depth information and estimate 3D trajectories from 2D projections, enabling real-time analytics on smartphones and tablets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple high-definition cameras and high-end hardware are used for real-time sports analytics, then measurement precision and reliability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts and isolates the essential function of trajectory detection from the complex multi-camera system, implementing it through a single mobile device with AI-based computer vision. This separates the core measurement function from the supporting infrastructure, achieving accurate trajectory reconstruction without requiring multiple cameras and high-end hardware setups.
Solution Approach 2:
The patent uses 2D video footage as a copy or representation of the 3D real-world scene, then applies AI algorithms to reconstruct the original 3D trajectories from this 2D copy. This allows the system to work with simple mobile device cameras while achieving professional-grade trajectory analysis that would traditionally require complex multi-camera systems.
2Measurement precision
If multiple cameras and complex setups are deployed for accurate 3D trajectory tracking, then measurement precision is improved, but ease of operation and adaptability deteriorate
Solution Approach 1:
The patent makes the mobile device perform multiple functions: it captures video, processes images, runs AI inference, and reconstructs 3D trajectories all within a single device. This universal approach eliminates the need for specialized camera setups and complex external equipment, making the system easy to operate and adaptable to various ball sports without requiring technical expertise for setup.
Solution Approach 2:
The system uses the mobile device's own built-in resources (camera, processor, AI models) to perform all trajectory analysis functions. The device serves itself by processing its own captured video data locally, eliminating the need for external cameras, servers, or complex infrastructure, thereby simplifying operation and deployment.
3Measurement precision
If dedicated sensors and complex triangulation techniques are used for depth information, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical depth-sensing hardware (such as LiDAR, time-of-flight sensors, or stereo camera setups) with AI-based computational methods. The system uses machine learning models to infer depth information from standard 2D video frames, substituting mechanical/optical depth-sensing systems with software-based solutions that run on mobile devices.
Solution Approach 2:
The patent introduces AI algorithms as an intermediary between the 2D video input and the 3D trajectory output. These algorithms act as a mediator that translates simple 2D visual data into accurate 3D spatial information without requiring direct 3D sensing hardware, thereby achieving professional-grade depth measurement using only a standard mobile camera.
Data Source
AI summary
Methods and systems for object trajectory reconstruction are disclosed. The methods and systems perform steps of first, receiving an input monocular video captured by a mobile computing device, and a camera projection matrix. Next, extracting a 2D trajectory of an object in an image plane of the input monocular video, and a posture of a user shooting the object along the 2D trajectory. Next, estimating a feasible shooting direction from the user posture and generating an on-ground projection line based on the feasible shooting direction and the 2D trajectory. Lastly, generating a 3D object trajectory based on the 2D trajectory, the on-ground projection line, and the camera projection matrix. 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.