Event-Based Image Capture Using Low-Resolution Frame Detection
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Solution Overview
Problem
Existing image capture methods struggle with capturing moving objects due to computational resource constraints, leading to missed opportunities and inefficient use of storage and processing power, especially when users manually track and trigger image capture or rely on real-time high-resolution analysis.
Innovation Solution
An electronic device analyzes low-resolution image frames in real-time to detect interested events, assigns visual scores, and retrieves high-resolution frames from a buffer based on these scores, allowing automatic capture with minimal latency and reduced resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time high-resolution image frame analysis is performed to detect events, then event detection accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the image processing task by analyzing only low-resolution image frames for event detection, while storing high-resolution frames separately. This division allows event detection to perform on computationally lighter low-resolution data, reducing resource consumption while maintaining detection capability.
Solution Approach 2:
The patent introduces low-resolution image frames as an intermediary for event detection. Instead of directly analyzing high-resolution frames, the system uses downsampled low-resolution frames as a mediator to detect events, thereby reducing computational load while preserving the ability to identify interesting events.
2Reliability
If continuous burst shots are captured to ensure interested scenes are recorded, then capture reliability is improved, but storage space consumption increases
Solution Approach 1:
The patent performs preliminary event detection on low-resolution frames before capturing high-resolution images. By pre-identifying interesting events through analysis of downsampled frames, the system can selectively capture only those moments that contain events of interest, avoiding unnecessary storage of non-event burst shots.
Solution Approach 2:
The system uses the low-resolution frame analysis to automatically determine when high-resolution capture should occur. The low-resolution analysis serves the function of guiding the high-resolution capture process, enabling the system to self-regulate storage usage based on actual event occurrence.
3Manufacturing precision
If manual tracking and triggering is used for image capture, then capture precision is improved, but operation complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the analysis of low-resolution frames provides real-time information about event occurrence. This feedback automatically triggers high-resolution capture when events are detected, eliminating the need for manual tracking and triggering while maintaining precise capture timing.
Solution Approach 2:
The system performs automatic event detection and triggers capture without requiring manual user intervention. The low-resolution frame analysis service automatically identifies events and initiates high-resolution capture, making the system self-sufficient and eliminating complex manual operations.
4Speed
If event detection is performed on low-resolution frames, then processing speed is improved, but event detection accuracy may deteriorate
Solution Approach 1:
The patent segments the processing pipeline into two stages: rapid event detection on low-resolution frames, followed by selective high-resolution capture. This segmentation allows the speed-critical detection phase to operate on compressed data while preserving accuracy by using the high-resolution frames for final capture when events are identified.
Solution Approach 2:
Low-resolution frames serve as an intermediary for preliminary event detection. While these frames provide faster processing, they act as a screening mechanism rather than the final detection authority. The intermediary low-resolution analysis guides the selection of which high-resolution frames to capture, maintaining overall detection accuracy.
Data Source
AI summary
A method of performing interested event based image capture by an electronic device includes receiving a plurality of image frames, storing the plurality of image frames in a high-resolution buffer, converting the plurality of image frames into a plurality of low-resolution image frames, detecting an interested event in at least one low-resolution image frame of the plurality of low-resolution image frames, determining a visual score of the interested event in the at least one detected low-resolution image frame, and obtaining at least one image frame corresponding to the at least one detected low-resolution image frame from the high-resolution buffer, based on the visual score.