Video Event Management System for Racetrack Coverage
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Solution Overview
Problem
Existing systems face challenges in efficiently tracking and recording moving objects of interest in large, live event spaces due to limitations in camera deployment, operator readiness, and storage capacity, particularly in capturing episodic events amidst long periods of inactivity.
Innovation Solution
The implementation of unmanned, high-resolution video cameras with stationary fields of view and a video event management system that includes an object locating and identifying unit and an event scoring unit, which automatically detect and analyze imagery data, generate subframes with metadata, and discard non-essential footage to conserve storage and focus on capturing interesting events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple camera operators are deployed to cover all spots in a large event space, then the coverage of episodic events is improved, but the cost and complexity of the system increases
Solution Approach 1:
The system uses automated computer vision algorithms to independently detect, track, and identify objects of interest across multiple camera feeds without human intervention. The automated event detection system autonomously determines when interesting events occur and notifies appropriate camera operators, eliminating the need for operators to manually monitor all spots simultaneously.
Solution Approach 2:
The patent replaces the mechanical system of multiple human operators manually monitoring and operating cameras with an automated electronic system using image processing algorithms, object detection models, and computer vision technology to perform the same functions automatically.
2Reliability
If all video footage is recorded for later review, then no interesting events are missed, but the storage capacity requirements become economically prohibitive
Solution Approach 1:
The system performs preliminary analysis of video footage in real-time using automated object detection and event detection algorithms before storage. By pre-identifying and flagging potentially interesting events, the system enables selective storage of only relevant footage rather than requiring storage of all footage, significantly reducing storage capacity requirements.
Solution Approach 2:
The patent extracts and separates interesting events from mundane footage using automated detection algorithms. Only the extracted interesting events are stored for later review, while boring footage is discarded or deleted, dramatically reducing the quantity of data that requires storage while ensuring no interesting events are missed.
3Measurement precision
If camera operators manually pan and adjust cameras to track moving objects, then the tracking accuracy is improved, but the response time to episodic events decreases
Solution Approach 1:
The system replaces manual mechanical camera panning and adjustment with automated computer-based object tracking algorithms that continuously analyze video feeds and automatically control camera positioning, enabling instant response to episodic events without human reaction delays.
Solution Approach 2:
The automated system maintains continuous monitoring and tracking of objects of interest without interruption, eliminating the start-stop nature of manual operation. The system is always ready to detect and respond to episodic events immediately as they occur, maximizing response time while maintaining tracking accuracy through continuous automated analysis.
Data Source
AI summary
Methods and systems for automatically tracking and analyzing imagery data of at least one vehicle on a racetrack comprising. A video event management system with a plurality of video cameras positioned around a racetrack determines the presence of the at least one vehicle and based on a weighted event score corresponding to dynamics for the at least one vehicle and other objects captures video imagery and stills and generates at least one subframe. Excess video imagery data and excess stills data are discarded based on metadata of linked subframes.


