Autonomous Photo Summary Generation via Image Sampling
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Solution Overview
Problem
Photographers often miss capturing important moments while focusing on a desired scene, and existing camera features like burst mode can overwhelm storage and require time-consuming manual review to select ideal photos.
Innovation Solution
A method for autonomously collecting and generating photo summaries by sampling images from a live stream based on predetermined features, such as motion interestingness, and outputting a curated selection of images as a summary, without user prompting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If burst mode is used to capture a series of photos rapidly, then the photographer can capture desired scenes, but the on-device storage is quickly filled up
Solution Approach 1:
The system extracts and removes duplicate or low-quality images from the burst sequence, keeping only the most valuable photos. This extraction process reduces the total number of images stored while preserving the essential content, directly resolving the contradiction between capturing rapid sequences and managing storage space.
Solution Approach 2:
The system automatically discards redundant images from burst sequences by detecting duplicates and low-quality frames, recovering storage space while maintaining the most important photos. This selective discarding allows continuous burst shooting without proportionally increasing storage requirements.
2Loss of information
If burst mode captures a series of photos, then more moments are captured, but manual review of the resulting sequence is time consuming and unwieldy
Solution Approach 1:
The system performs automatic curation and selection of the best photos from burst sequences without requiring user intervention. By implementing self-service functionality that autonomously identifies and selects the most valuable images, the system eliminates the time-consuming manual review process while preserving all captured moments.
Solution Approach 2:
The system replaces the mechanical process of manual photo review with automated computational algorithms that analyze and select images based on quality metrics, motion detection, and duplicate identification. This substitution transforms the time-intensive manual curation task into an instantaneous automated process.
3Reliability
If video is shot and frames are extracted for still images, then ideal scenes can be captured, but storage space and image curation issues become even more severe
Solution Approach 1:
Instead of processing the entire video stream, the system applies partial action by selectively extracting and analyzing only key frames or segments that contain the ideal scenes. This approach maintains scene capture accuracy while significantly reducing the storage and processing burden compared to handling complete video data.
Data Source
AI summary
Implementations of the disclosed technology include techniques for autonomously collecting image data, and generating photo summaries based thereon. In some implementations, a plurality of images may be autonomously sampled from an available stream of image data. For example, a camera application of a smartphone or other mobile computing device may present a live preview based on a stream of data from an image capture device. The live stream of image capture data may be sampled and the most interesting photos preserved for further filtering and presentation. The preserved photos may be further winnowed as a photo session continues and an image object generated summarizing the remaining photos. Accordingly, image capture data may be autonomously collected, filtered, and formatted to enable a photographer to see what moments they missed manually capturing during a photo session.