Camera Focus Tracking for Multi-Window Video Recording
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Solution Overview
Problem
Manual editing of videos recorded by terminal devices with multiple cameras is complex and requires specific video editing techniques, leading to a poor user experience.
Innovation Solution
A recording method and apparatus that utilizes focus tracking to automatically identify and display a selected object in multiple windows during video recording, reducing the need for manual editing by continuously tracking the object and adjusting display states based on object presence, and providing user-friendly interfaces for improved interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual editing processing is used to obtain video of target character, then video editing can be performed, but the operations become complex and user experience deteriorates
Solution Approach 1:
The system performs preliminary action by automatically detecting and tracking the target character during video recording, pre-processing the video data to identify and extract the target character's video segments before the user needs them. This eliminates the need for complex post-recording manual editing operations.
Solution Approach 2:
The system implements self-service by automatically performing video editing functions through intelligent detection and tracking algorithms. The terminal device itself identifies the target character, tracks their movements, and extracts relevant video segments without requiring external editing software or manual user intervention.
2Productivity
If focus tracking is implemented to automatically display selected object, then manual editing operations are reduced, but the system complexity increases
Solution Approach 1:
The terminal device integrates multiple functions including detection, tracking, and video processing within a unified focus tracking system. This multi-functional approach allows the device to automatically identify target characters, track their positions across frames, and extract video segments, thereby improving productivity while managing system complexity through functional integration.
Solution Approach 2:
The system replaces manual mechanical editing operations with automated intelligent algorithms. Instead of requiring users to manually edit videos frame by frame or segment by segment, the patent employs detection and tracking algorithms that automatically identify and extract target character videos, substituting mechanical user actions with automated computational processes.
3Loss of information
If multiple windows are used to display different pictures, then more information is shown, but the interface complexity increases
Solution Approach 1:
The interface is segmented into multiple windows, each serving a specific function: one window displays the main video feed with target character detection, another window shows the extracted target character video, and additional windows may display tracking information or controls. This segmentation allows comprehensive video information to be displayed simultaneously while organizing the interface into manageable, functionally-distinct sections.
Solution Approach 2:
The system transitions from a single-dimension linear interface to a multi-dimensional spatial layout with multiple overlapping windows. By arranging video feeds, controls, and information displays in a two-dimensional windowed environment, the interface can present more information simultaneously without requiring users to navigate through multiple screens or complex hierarchical menus.
Data Source
AI summary
A recording method which includes: displaying a first interface of a camera application; at a first moment, when detecting that a first position of a first picture includes a first object, including the first object in a second picture; at a second moment, when detecting that a second position of the first picture includes the first object, including the first object in the second picture; at a third moment, when detecting that the first picture does not include the first object, skipping including the first object in the second picture as well; at a first moment, when detecting that a third position of the first picture includes the first object, including the first object in the second picture; and at a fifth moment, when detecting that a fourth position of the first picture includes the first object, including the first object in the second picture.