Tracking System Using Motorized Mirrors for High-Resolution Object Detection
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Solution Overview
Problem
Current tracking systems for athletes and objects in large-scale environments, such as sports fields, face limitations in spatial resolution and accuracy, particularly in real-time monitoring and high-level event description, due to the sparse distribution of objects and the need for labor-intensive manual analysis.
Innovation Solution
A tracking system utilizing a combination of panoramic and zoomed cameras, motorized mirrors, secondary shutters, and optional LIDAR and microphones, synchronized via a network connection, to maximize signal-to-noise ratio and provide high-resolution, real-time tracking of objects with adjustable field of view and speed measurements, enabling detailed movement analysis and 3D reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If a camera overviewing the entire field is used, then the coverage area is maximized, but the spatial resolution is reduced to the size of the field divided by camera resolution
Solution Approach 1:
The system divides the tracking field into multiple zones, each monitored by a dedicated zoomed camera. Instead of using one camera to view the entire field, multiple cameras are positioned to cover specific segments of the field, allowing each camera to provide high-resolution views of its assigned zone while collectively covering the entire area.
Solution Approach 2:
The system transitions from a single two-dimensional camera view to a three-dimensional arrangement of multiple cameras at different positions and angles. By adding spatial dimensions to the camera configuration, the system achieves both wide coverage and high resolution simultaneously through multi-perspective observation.
2Measurement precision
If a zoomed camera focused on the object of interest is used, then the spatial resolution is improved, but the tracked object may leave the field of view
Solution Approach 1:
The system dynamically assigns different zoomed cameras to track different objects based on their positions and movements. As objects move between zones, the system adapts by switching which camera focuses on which object, ensuring continuous high-resolution tracking without objects leaving the field of view.
Solution Approach 2:
The system uses feedback from object position detection to dynamically adjust camera focus and assignment. When an object approaches the boundary of a camera's field of view, the system detects this and reassigns tracking to an adjacent camera before the object leaves the view, maintaining continuous high-resolution monitoring.
3Device complexity
If manual analysis is used for high-level event description, then the system is simple to implement, but it is labor intensive and leads to inaccuracies due to human error
Solution Approach 1:
The system implements automated computer vision algorithms that independently perform detection, tracking, and event analysis without human intervention. The algorithms process camera data to automatically generate high-level event descriptions, eliminating the need for manual analysis while improving both efficiency and accuracy.
Solution Approach 2:
The system replaces the mechanical process of manual human analysis with automated computational algorithms. Instead of human operators watching and analyzing video footage, computer vision algorithms automatically process the data, substituting mechanical human labor with automated computational processing.
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 accurate, real-time tracking of athletes and objects with high spatial resolution, reducing human error and enhancing the ability to analyze game events, providing detailed movement data and sound localization, thus improving the accuracy and efficiency of tracking in large-scale environments.
Implementation Method 1
auxiliary LIDAR and/or at least one camera overviewing the entire field
Data Source
AI summary
A modular tracking system is described comprising of the network of independent tracking units optionally accompanied by a LIDAR scanner and/or (one or more) elevated cameras. Tracking units are combining panoramic and zoomed cameras to imitate the working principle of the human eye. Markerless computer vision algorithms are executed directly on the units and provide feedback to motorized mirror placed in front of the zoomed camera to keep tracked objects/people in its field of view. Microphones are used to detect and localize sound events. Inference from different sensor is fused in real time to reconstruct high-level events and full skeleton representation for each participant.