Follow Spotlight Tracking With Vision-Guided Manual Position Correction
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Solution Overview
Problem
Existing methods for manually tracking a target object in a performance lighting system face challenges such as inaccurate positioning due to manual control, interference from tags, and loss of the target object in complex scenarios, leading to poor accuracy and jitter.
Innovation Solution
A method utilizing machine vision and a lighting control system to track a target object by combining predicted state information with manually inputted real coordinate positions, employing Kalman filtering to refine the tracking process, and using a rectangular frame to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual control is used to rotate the follow spotlight, then the lighting engineer can control the spotlight rotation, but the positioning accuracy deteriorates due to far distance between the follow spotlight and the stage
Solution Approach 1:
A camera is introduced as an intermediary device to capture the target object's position, and a computing device processes this visual information to calculate precise coordinates. This intermediary system bridges the gap between manual operation and accurate positioning, allowing the lighting engineer to control the spotlight while the camera-computing system handles the precise measurement function.
Solution Approach 2:
The manual mechanical control system is replaced with an automated vision-based system. Instead of relying on physical manual adjustment, the patent uses camera imaging and computational algorithms to automatically determine the target object's position and calculate the required spotlight rotation angles, substituting mechanical operation with optical and computational processes.
2Measurement precision
If active tags or passive tags are used for tracking, then the target object can be positioned, but the positioning accuracy deteriorates due to interference or blocking by objects or surrounding scenes
Solution Approach 1:
The patent extracts the tracking function from physical tags and implements it through visual recognition. By using a camera to capture images and computational algorithms to identify and track the target object's position, the system eliminates the need for tags that are susceptible to interference and blocking, thereby removing the harmful factors affecting positioning accuracy.
Solution Approach 2:
Instead of using physical tags that can be blocked, the system creates a visual copy or representation of the target object through camera imaging. The computing device processes this visual copy to determine position, allowing tracking without physical contact or line-of-sight requirements, thus avoiding interference and blocking issues.
3Measurement precision
If a camera is used to identify and track the target object, then positioning can be achieved, but the system loses the target object in complex scenarios with multiple people or similar shapes
Solution Approach 1:
The patent applies local quality differentiation by using distinctive visual features of the target object (such as unique colors, patterns, or shapes) to enable reliable identification. By focusing on these specific local characteristics rather than general appearance, the system can distinguish the target from other objects in complex scenes, maintaining stable tracking even when multiple people or similar shapes are present.
4Device complexity
If manual tracking is performed without assistance, then the system is simple, but the positioning accuracy and continuity deteriorate due to tiny actions of the input device causing spot jitter
Solution Approach 1:
The patent implements a feedback mechanism where the computing device continuously receives input from the input device, processes it through visual recognition algorithms, and provides corrected position information back to control the spotlight. This feedback loop compensates for tiny actions or inaccuracies in manual input, maintaining high positioning accuracy while keeping the overall system relatively simple.
Data Source
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AI summary
A method for manually tracking a target object with aid of machine vision includes steps of: taking an image containing a target object by a camera with a known pose; calculating predicted state information of the target object containing a predicted coordinate position at a tth moment according to movement state information of the target object at a (t-1)th moment; taking a position of the target object at the tth moment manually input by a user as a real coordinate position; obtaining movement state information of the target object at the tth moment by combining the real coordinate position with the predicted state information; calculating a spatial coordinate position of the target object according to an estimated coordinate position contained in the movement state information by combining parameters of the camera; and sending the spatial coordinate position to a follow spotlight for tracking.