Time-Synchronized Camera Array for Occlusion-Resistant Object Tracking
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Solution Overview
Problem
Current camera systems and computer vision technologies are inadequate for tracking objects, particularly large objects like soccer balls, at close ranges within enclosed spaces due to occlusion issues and lack of time-synchronization, which hinders accurate triangulation and analysis of object movements in sports training and entertainment applications.
Innovation Solution
A system comprising a plurality of time-synchronized cameras positioned in a mirrored configuration around the enclosed space to capture images simultaneously, allowing for effective triangulation of object location and tracking, even at close distances, and generating graphical visualizations based on object interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera systems are used to track objects in enclosed spaces, then the system complexity is low, but the measurement precision and tracking accuracy deteriorate due to occlusion issues and lack of time-synchronization
Solution Approach 1:
The system divides the enclosed space into multiple zones with cameras positioned at different locations, each capturing specific regions. This segmentation allows tracking of objects even when partially occluded, as different camera segments provide complementary views that together maintain complete object visibility and measurement precision.
Solution Approach 2:
The cameras are pre-positioned in strategic locations around the enclosed space and time-synchronized before tracking begins. This preliminary configuration ensures that when an object enters the space, the camera network is already optimized for immediate accurate tracking without occlusion issues, eliminating the need for real-time system reconfiguration.
2Area of stationary object
If cameras are positioned close to capture detailed images of large objects like soccer balls, then the area of detection is improved, but occlusion issues worsen making triangulation difficult
Solution Approach 1:
The system transitions from single-camera 2D detection to multi-camera 3D triangulation by positioning cameras at different spatial locations around the enclosed space. This dimensional approach allows the system to detect objects in three dimensions and resolve occlusion issues by selecting optimal camera views that provide unobstructed lines of sight to the object from multiple angles.
Solution Approach 2:
The system introduces time-synchronization as an intermediary mechanism that coordinates camera shutters to capture images at identical moments. This intermediary function ensures that even when objects move quickly or change position between captures, the multi-camera system maintains spatial consistency and eliminates occlusion problems by capturing all views simultaneously rather than sequentially.
3Measurement precision
If multiple cameras are used to overcome occlusion and improve triangulation, then the measurement precision is improved, but the device complexity and synchronization requirements increase
Solution Approach 1:
The system implements a centralized time-synchronization mechanism that provides feedback signals to all cameras, ensuring they capture images at precisely coordinated moments. This feedback loop continuously monitors and adjusts camera timing, maintaining synchronization accuracy despite variations in camera hardware or environmental conditions, thereby enabling reliable multi-camera triangulation without excessive complexity.
4Ease of operation
If traditional video recording is used for training review, then the ease of operation is maintained, but the loss of information occurs as players cannot understand proper mechanics or determine improvement
Solution Approach 1:
The system replaces traditional passive video recording with an active computer vision analysis system that automatically processes captured images to extract meaningful training metrics. Instead of merely playing back video, the system uses image processing algorithms to detect object positions, trajectories, and interactions, then presents this processed information in formats that provide actionable feedback while maintaining ease of operation through automated analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise tracking and analysis of object movements, improving training accuracy and providing enhanced entertainment experiences by overcoming occlusion and synchronization limitations of existing systems.
Implementation Method 1
A system comprising a plurality of time-synchronized cameras positioned in a mirrored configuration around the enclosed space to capture images simultaneously, allowing for effective triangulation of object location and tracking
Data Source
AI summary
Disclosed herein is a system and method directed to object tracking and metric generation using a plurality of cameras. The system includes the plurality of cameras disposed around a playing surface in a mirrored configuration, where the plurality of cameras are time-synchronized. The system further includes logic that, when executed by a processor, causes performance of operations including: obtaining a sequence of images from a plurality of cameras, determining a subsection of a first image of the sequence of images having a highest probability of including the object, detecting the object within the subsection of the first image through object recognition processing, triangulating a location of the object within the playing space using the first image and the second image, and determining a subsection of a third image of the sequence of images having a highest probability of including the object based on the triangulated location of the object.