Motorized Camera Gimbal Tracking for Blur-Free Surround Imaging
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Solution Overview
Problem
Existing camera stabilizers rely on human operation, leading to inaccuracies and blurry images due to imperfect movement, which affects the quality of photographs and videos.
Innovation Solution
An automated gimbal system that uses a motorized gimbal controlled by a computerized algorithm to stabilize the camera by estimating the center of an object and adjusting its position, utilizing a predictive algorithm and neural network to generate a bounding box and adjust the gimbal's focus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a manual camera stabilizer is operated by human, then the device can be controlled and adjusted, but the stabilization accuracy deteriorates due to inaccuracies and anomalies in human movement
Solution Approach 1:
The patent replaces the manual mechanical control system with an automated motorized gimbal system. The motorized gimbal uses electronic motors and control algorithms to stabilize the camera, eliminating human movement inaccuracies. The system processes image data through neural networks and predictive algorithms to automatically adjust the gimbal position, substituting mechanical human operation with an automated electromechanical system that achieves superior stabilization accuracy.
2Measurement precision
If an automated gimbal system is implemented, then image stabilization quality is improved, but device complexity increases due to motorized components and computerized algorithms
Solution Approach 1:
The patent integrates multiple functions into a unified automated gimbal system. The motorized gimbal simultaneously performs camera stabilization, automated panning, and tracking of objects of interest. The computerized algorithm processes image data, generates bounding boxes using neural networks, predicts object motion, and controls gimbal movement all through a single integrated system. This multi-functionality reduces the need for separate devices while managing complexity through consolidation.
Solution Approach 2:
The automated gimbal system operates autonomously without requiring manual intervention. The computerized algorithm automatically processes incoming image data, identifies objects of interest through neural network analysis, predicts their future positions, and adjusts the gimbal accordingly. The system serves itself by continuously adapting to scene changes and maintaining optimal framing, eliminating the need for human operators while managing complexity through self-contained automation.
3Device complexity
If human operators manually stabilize the camera, then the device structure remains simple, but the final image quality deteriorates due to shaky and blurry images
Solution Approach 1:
The patent replaces simple manual mechanical stabilization with an automated motorized gimbal system that uses electronic motors, sensors, and computerized control algorithms. This substitution transforms the stabilizer from a passive mechanical device to an active electromechanical system capable of precise, dynamic adjustments. The motorized gimbal compensates for camera movements with high precision, eliminating the shaky and blurry images produced by manual operation while maintaining a relatively streamlined device structure.
Data Source
AI summary
The method includes capturing a first portion of a surround image using a camera; generating a bounding box using an image in the first portion of the surround image; estimating a center of an item in the image using the bounding box; and adjusting a position of a motorized gimbal such that the motorized gimbal focuses on the center of the item.