Roulette Wheel Video Sync Using Sensor-Embedded Rotational Data
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Solution Overview
Problem
Existing methods for synchronizing image data with the physical movement of a roulette wheel are computationally expensive and time-consuming, making them unsuitable for high-quality, real-time applications.
Innovation Solution
A roulette wheel system that includes sensors on the underside to track the rotation of pockets, a computer to extrapolate rotational data from sensor input, and embed this data into video stream, enabling synchronized graphical overlays on player stations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image capture, recognition and synthesis analysis are used to track the roulette ball and pockets, then image data can be synchronized with wheel movement, but the process becomes computationally expensive and time-consuming
Solution Approach 1:
The patent applies preliminary action by pre-attaching machine-readable indicators (MRTags) to the roulette wheel pockets and using optical encoders to pre-establish reference points. This allows the system to track wheel position through simple marker detection rather than performing complex real-time image analysis, thereby achieving accurate synchronization without computational delays
Solution Approach 2:
The patent introduces intermediary elements (MRTags and optical encoders) that serve as mediators between the physical wheel and the digital tracking system. These intermediaries provide easily detectable reference signals that bridge the physical rotation of the wheel with the video feed synchronization, eliminating the need for expensive image recognition algorithms
2Measurement precision
If complex image analysis and processing are performed to track the roulette ball and pockets, then synchronization can be achieved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent extracts the tracking problem from complex image analysis by isolating specific, easily detectable features (MRTags and encoder positions) from the overall wheel image. Instead of analyzing the entire complex scene, the system only needs to detect these simple, pre-defined markers, dramatically reducing computational requirements while maintaining tracking accuracy
Solution Approach 2:
The patent uses visual copies (MRTags) that replicate the position information of the pockets in a simplified, machine-readable format. These tags are optical copies of the pocket positions that can be detected instantly without requiring full image recognition, thereby reducing computational complexity while preserving tracking accuracy
Data Source
AI summary
A roulette wheel having a rotating roulette wheel including upper and bottom surfaces, pockets on the upper surfaces and symbols arranged around and corresponding to the pockets. Triggers on the bottom surfaces trigger sensors. A computer receives sensor data and image data from video images of the upper surfaces, extrapolates rotational data associated with a location of each pocket from the sensor data, outputs the rotational data embedded in the image data, and generates random data. Player stations enable players to play by selecting symbols and generating playing data based on the symbol selection. Each player station receives the rotational data embedded in the image data, generates graphical data based on the random data and the playing data, and displays the graphical data on a display overlayed on the image data and synchronized to movement of the upper surfaces in the image data based on the rotational data.