Sign Language Video Framing with Predictive Keypoint Tracking
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Solution Overview
Problem
Existing methods for capturing sign language videos require manual adjustment of camera position and distance to ensure the user's hands remain in the frame, leading to complexity and reduced efficiency in video shooting.
Innovation Solution
A method that identifies human body key points in sign language actions, predicts future key point coordinates, and adjusts photographing parameters to maintain the user within the camera frame without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of camera position and distance is used to ensure user's hands remain in frame, then sign language video accuracy is improved, but shooting process complexity increases
Solution Approach 1:
The system automatically tracks the user's hand movements and adjusts photographing parameters without manual intervention. The processor detects hand key points in real-time and autonomously controls camera positioning and focus, eliminating the need for manual adjustment while maintaining high accuracy in capturing sign language expressions.
Solution Approach 2:
The system continuously monitors the user's hand position through key point detection and uses this feedback to dynamically adjust photographing parameters. The processor analyzes hand movement trajectories and automatically modifies camera settings to keep hands within the optimal framing range, creating a closed-loop control system that maintains accuracy without user intervention.
2Measurement precision
If manual adjustment of camera position and distance is used to ensure user's hands remain in frame, then sign language video accuracy is improved, but shooting efficiency decreases
Solution Approach 1:
The system maintains continuous automatic tracking and adjustment of photographing parameters throughout the sign language video recording. The processor continuously detects hand key points and dynamically adjusts camera settings without interruption, ensuring hands remain in optimal frame position throughout the entire shooting process, thereby maintaining both accuracy and efficiency.
Solution Approach 2:
The automatic parameter adjustment system operates autonomously during the entire video recording process, eliminating the need for manual intervention. The system self-adjusts camera position, focus, and framing based on real-time hand detection, allowing users to concentrate on performing sign language expressions rather than adjusting equipment, thus significantly improving shooting efficiency.
3Ease of operation
If automatic photographing parameter adjustment is implemented, then shooting process complexity is reduced, but system complexity increases
Solution Approach 1:
The system introduces an intelligent processor as an intermediary between the user and the camera system. This processor handles the complex tasks of key point detection, trajectory analysis, and parameter adjustment, shielding users from technical complexity while providing simple, automatic operation. The intermediary processes computational complexity internally while presenting a simplified interface to users.
Solution Approach 2:
The system replaces manual mechanical adjustment with automated computational control. Instead of requiring users to physically adjust camera position and focus, the system uses image processing algorithms and automated motor control to achieve the same results, transferring complexity from the operational interface to the computational and control systems.
4Measurement precision
If two users cooperate for sign language video shooting, then hand visibility is improved, but shooting process complexity increases
Solution Approach 1:
The system uses self-service automatic detection and tracking to monitor and adjust for hand position throughout the video recording. The processor continuously identifies hand key points and autonomously adjusts photographing parameters to maintain optimal hand visibility, eliminating the need for a second user to manually assist with positioning and framing.
Data Source
AI summary
A photographing parameter adjustment method and apparatus, an electronic device, and a readable storage medium are provided. The method includes: obtaining a first image in a video, where the first image includes a shooting object performing a first sign language action, and the first sign language action corresponds to a first human body key point of the shooting object; determining first sign language information based on first coordinate information of the first human body key point in the first image, where the first sign language information is used for representing a body pose, an action trajectory, and a facial morphology of the shooting object when the shooting object performs the first sign language action; predicting second coordinate information based on the first coordinate information and the first sign language information; and adjusting a photographing parameter based on the second coordinate information.