3D Ball Tracking via Single Camera and Physics Model
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Solution Overview
Problem
Current 3D ball tracking technologies are inaccessible to lower-level sports and recreational users due to their high cost, complex installation, and maintenance requirements, as well as limitations in accuracy and hardware needs, especially for small balls or high-motion scenarios.
Innovation Solution
A system that uses a single camera, such as a smartphone, to track a ball in 3D by computing the 3D track of a ball moving in a gaming environment, employing techniques like camera projection matrix generation, neural networks for ball detection, and 3D physics models to estimate the ball's position, allowing for accurate tracking with minimal hardware and easy operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple high-speed cameras with optic fiber cables are used for triangulation, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent extracts only the essential function of ball tracking from the complex multi-camera system and implements it using a single camera. By removing unnecessary cameras, optic fiber cables, and synchronization hardware, the system achieves the same tracking capability with minimal hardware while maintaining measurement precision through computational methods rather than physical triangulation
Solution Approach 2:
The patent replaces the mechanical triangulation system with a computational approach. Instead of using multiple cameras positioned in specific geometries to physically triangulate ball position, the system uses a single camera and computational algorithms (including neural networks and physics models) to calculate the 3D trajectory, thereby eliminating complex mechanical hardware while maintaining tracking accuracy
2Measurement precision
If multiple cameras operating at high frames per second are used, then measurement precision is improved, but use of energy and device complexity increase
Solution Approach 1:
The patent extracts the ball tracking function from the energy-intensive multi-camera high-frame-rate system and implements it using a single camera at standard frame rates. By removing multiple cameras and their associated power consumption, the system achieves comparable tracking precision with significantly reduced energy usage
Solution Approach 2:
The patent creates a computational copy of the 3D tracking function that replaces the physical multi-camera system. Instead of capturing simultaneous images from multiple cameras at high frame rates, the system uses a single camera feed and processes it through neural networks and physics-based models to reconstruct the 3D trajectory, thereby reducing energy consumption while maintaining measurement precision
3Device complexity
If a single camera is used for tracking, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent replaces the mechanical multi-camera triangulation system with a computational approach using a single camera. By substituting physical triangulation hardware with neural networks and physics-based computational models, the system achieves 3D tracking accuracy comparable to or better than traditional methods while using minimal hardware
Solution Approach 2:
The patent transitions from 2D image capture to 3D trajectory reconstruction by adding computational dimensions. The single camera captures 2D images, but through neural networks and physics models, the system reconstructs the 3D ball trajectory, effectively adding the third dimension through computation rather than additional hardware
4Measurement precision
If existing 3D tracking solutions are used for small balls or high-motion scenarios, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the ball detection and tracking function from complex multi-camera systems and implements it using a single camera with specialized neural networks. By removing unnecessary cameras and hardware, the system maintains the ability to track small balls and high-motion scenarios with high precision while significantly reducing device complexity
Data Source
AI summary
Systems and methods are described for generating a three-dimensional track a ball in a gaming environment from a single camera. In some examples, an input video including frames of a ball moving in a gaming environment recorded by a camera may be obtained, along with a camera projection matrix associated with at least one frame that maps a two-dimensional pixel space representation to a three-dimensional representation of the gaming environment. Candidate two-dimensional image locations of the ball across the plurality of frames may be identified using a neural network or a computer vision algorithm. An optimization algorithm may be performed that uses a 3D ball physics model, the camera projection matrix and a subset of the candidate two-dimensional image locations of the ball to generate a three-dimensional track of the ball in the gaming environment. The three-dimensional track of the ball may then be provided to a user device.