UAV Video Abstraction via Trigger-Based Keyframe Selection
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Solution Overview
Problem
Existing systems for movable objects, such as UAVs, face challenges in efficiently and accurately processing image data to create video abstraction, especially in real-time, as the amount of data captured increases, and in identifying content of interest amidst operational variations.
Innovation Solution
A method involving an unmanned aerial vehicle (UAV) equipped with sensors and an imaging device, where trigger events detected by sensors determine key image frames of interest, and adaptive selection of image frames is performed based on comparisons of temporally adjacent frames to create efficient and accurate video abstraction, using processors and stored programs for real-time or post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all captured image frames are processed and stored, then complete video record is achieved, but data storage requirements and processing time increase significantly
Solution Approach 1:
The patent extracts only the most representative image frames from the continuous video stream by identifying keyframes based on trigger events and similarity comparisons. Instead of storing all frames, the system selectively extracts and stores only those frames that contain significant operational information, thereby reducing data storage requirements while preserving essential video content.
Solution Approach 2:
The system performs partial processing by focusing computational resources on identifying and processing only the most important frames rather than uniformly processing all frames. This selective approach applies processing power selectively to frames that meet specific criteria (trigger events, dissimilarity thresholds), achieving effective video abstraction with reduced computational burden.
2Measurement precision
If all captured image frames are processed, then accurate video abstraction is achieved, but processing speed and real-time capability decrease
Solution Approach 1:
The patent segments the continuous video processing task into discrete frame-by-frame analysis units. Each frame is independently evaluated against trigger events and similarity thresholds, allowing parallel processing and real-time decision-making. This segmentation enables the system to maintain high processing speed while ensuring accurate identification of keyframes through systematic comparison.
Solution Approach 2:
The system performs preliminary filtering by first identifying trigger events and then only processing frames that meet specific dissimilarity thresholds. This preliminary action eliminates the need for exhaustive processing of all frames, significantly improving processing speed while maintaining abstraction accuracy through targeted analysis of only relevant frames.
3Productivity
If frame selection is based on simple timing intervals, then processing speed is maintained, but accuracy in identifying content of interest decreases
Solution Approach 1:
The patent implements dynamic frame selection criteria that adapt to changing operational conditions. Instead of fixed time intervals, the system dynamically determines which frames to select based on real-time trigger events and comparative analysis with previous keyframes. This dynamic approach ensures high accuracy in identifying content of interest while maintaining processing efficiency through event-driven selection.
Solution Approach 2:
The system uses feedback from comparative analysis between consecutive keyframes to dynamically adjust frame selection. By continuously comparing new frames against previously selected keyframes and using dissimilarity metrics as feedback, the system accurately identifies when significant changes occur, ensuring precise content identification without relying on predetermined time intervals.
4Measurement precision
If complex analysis is performed on all image frames, then identification accuracy of content of interest improves, but computational load and energy consumption increase
Solution Approach 1:
The patent extracts computational analysis only from frames that meet specific selection criteria (trigger events and dissimilarity thresholds) rather than performing complex analysis on all frames. This selective extraction approach maintains high detection accuracy for important content while dramatically reducing overall energy consumption by avoiding unnecessary processing of routine frames.
Solution Approach 2:
The system changes the operational parameters of frame processing by applying different levels of analysis based on frame importance. Frames triggered by events or showing significant dissimilarity undergo complex analysis with high computational resources, while other frames receive minimal or no processing. This parameter-based differentiation maintains detection accuracy for critical content while optimizing energy consumption across the entire video stream.
Data Source
AI summary
A method for processing image data captured by an imaging device borne on a movable object includes receiving a plurality of trigger events each corresponding to an operational condition variation detected by a first sensing device borne on the movable object, identifying, among a sequence of image frames captured by the imaging device, a plurality of image frames of interest each determined by one of the plurality of trigger events, and adaptively selecting, from the sequence of image frames, a set of image frames in accordance with a comparison of a plurality of temporally adjacent image frames of the plurality of image frames of interest.