Ghost Driver Overlay for Auto Racing Video Analytics
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Solution Overview
Problem
Current auto racing broadcasts lack video enhancement and statistical data, limiting the viewing experience and fan engagement for auto racing events.
Innovation Solution
A cloud-based server platform that collects and analyzes real-time data from auto racing events, allowing for performance comparisons and video enhancements by overlaying ghost drivers/vehicles onto live video feeds, along with customizable graphical user interfaces for displaying statistical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional broadcast methods are used, then the broadcasting system remains simple and easy to operate, but the viewing experience lacks video enhancement and statistical data
Solution Approach 1:
A server acts as an intermediary between the broadcast system and the viewing platform. The server collects raw data from multiple sources, processes it into statistical information, and delivers it to the broadcast system for integration with video feeds. This intermediary approach adds information capabilities without requiring direct complexity in the broadcast infrastructure.
Solution Approach 2:
The system creates digital copies of racing data from multiple sensors and cameras, processes these copies into statistical information, and overlays them onto the video broadcast. This allows the original broadcast system to remain unchanged while adding enhanced information layers through digital copying and processing.
2Ease of operation
If video enhancement with ghost drivers/vehicles is implemented, then fan engagement and viewing experience improve, but the processing complexity and data requirements increase
Solution Approach 1:
The system performs preliminary processing of racing data by pre-calculating statistical information, positioning data for ghost drivers/vehicles, and preparing overlay graphics before the broadcast. This advance preparation reduces real-time processing complexity while maintaining high engagement value during the actual broadcast.
Solution Approach 2:
The system adds a temporal dimension to video enhancement by incorporating historical racing data and comparisons across different time periods. Ghost drivers/vehicles from previous races are overlaid onto current broadcasts, creating multi-temporal visual comparisons that enhance fan engagement without requiring complex spatial modifications to the broadcast infrastructure.
3Loss of information
If real-time data collection and analysis are performed, then performance comparisons become available, but the data processing time and computational resources increase
Solution Approach 1:
The system implements periodic data collection and processing cycles, updating statistical information at regular intervals during the race rather than attempting continuous real-time processing. This periodic approach ensures fresh performance data is available while managing computational resources efficiently by processing in discrete batches.
Solution Approach 2:
The system performs preliminary data validation, filtering, and structuring as data is collected from sensors and cameras. By preparing and organizing raw data in advance before detailed analysis, the system reduces the computational time required for performance comparisons while ensuring data accuracy and completeness.
Data Source
AI summary
Systems and methods for video presentation and analytics for a sporting event are disclosed. In one embodiment, the sporting event is an auto racing event. A server platform is provided to collect and analyze real-time raw data and historical raw data, and compare drivers/vehicles from a current auto racing event and/or a historical auto racing event. The server platform is operable to overlay a ghost driver/vehicle on the images of a driver/vehicle in the current auto racing event based on the comparison. The server platform also provides a GUI for displaying the current auto racing event with enhanced features.