Video Recording With Automatic Object And Scene Labeling
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Solution Overview
Problem
Existing image recording devices require manual control for starting and stopping photography, leading to inefficient video management and editing, causing user disinterest due to the time-consuming process of sorting through large datasets.
Innovation Solution
A video recording system with a camera module, processing unit, and neural networks for automatic object detection, labeling, and scene analysis, enabling intelligent video management and filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual control is used for starting and stopping photography, then users can control when to capture images, but the process becomes time-consuming and inefficient for managing large volumes of video data
Solution Approach 1:
The system performs automatic object detection, scene analysis, and video clipping without user intervention. The processing unit automatically identifies objects in video frames, determines image scenes, generates candidate clips, and attaches labels, enabling the system to serve itself rather than requiring manual user control for each operation.
Solution Approach 2:
The system performs preliminary analysis by detecting objects and determining scenes before generating final video clips. By pre-processing the video data to identify key elements and potential clip boundaries, the system prepares the data in advance, reducing the time needed for subsequent user review and editing.
2Measurement precision
If users review video clip by clip using advanced image processing software, then they can find meaningful clips, but the process is extremely time-consuming and causes user disinterest
Solution Approach 1:
The system performs preliminary object detection and scene analysis on all video frames before user review. By pre-identifying objects, determining scenes, and generating candidate clips with labels, the system prepares filtered results in advance, allowing users to review only relevant clips rather than examining every frame manually.
Solution Approach 2:
The processing unit acts as an intermediary between the raw video data and the user. It automatically detects objects, determines scenes, generates candidate clips, and attaches labels, serving as a mediator that filters and organizes video data before presenting it to users for final review.
3Quantity of substance
If users save recorded images to hard disk for future use, then they can preserve the content, but they give up because the hard disk cannot store too much data
Solution Approach 1:
The system extracts only the meaningful and relevant video clips from the entire recorded video data based on detected objects and scenes. By identifying and separating significant content (candidate clips with labels) from the bulk data, the system reduces the storage requirement while preserving the most valuable content for future use.
Solution Approach 2:
The system changes the storage parameter from storing all raw video data to storing only filtered candidate clips with attached labels. By transforming the data from complete video files to selected, labeled clips, the system increases storage efficiency and flexibility while maintaining adaptability for various future uses.
Data Source
AI summary
A video recording system includes a camera, a memory device, and a processor. The memory device is for storing at least one script code. The processor is electrically connected to the camera and the memory device, and for performing at least following steps when reading the at least one script code: capturing a plurality of images through the camera to generate a video; identifying at least one generic object in a plurality of frames in the video; determining an image scene according to the at least one generic object; performing a specific object detection according to the image scene to determine whether at least one specific object or at least one specific event appears in the frames; and attaching a label to at least one of the frames with the at least one specific object or the at least one specific event.