Automated Video Classification for Memory Conservation
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Solution Overview
Problem
Users face challenges in managing and conserving video content captured on mobile devices due to the large memory requirements, as existing systems lack efficient methods to automatically extract relevant media items like still images or video sequences that represent the intended experiences.
Innovation Solution
A system and method that classify captured videos based on feature extraction from object detection, scene recognition, and motion analysis to generate media items, distinguishing between static and dynamic events, and determining the quality of frames to replace still images with higher quality frames if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all captured videos are stored in full resolution, then complete video content is preserved, but memory consumption becomes excessive
Solution Approach 1:
The system extracts only the most significant portions of video content (highlight clips) rather than storing complete videos. It identifies and extracts key moments based on motion analysis, scene changes, and object detection, storing only these extracted segments at full resolution while summarizing the rest, thereby reducing memory consumption while preserving important content.
Solution Approach 2:
The video content is segmented into different quality levels: highlight segments stored at full resolution and non-highlight segments stored as lower-quality summaries or thumbnails. This segmentation allows the system to prioritize storage resources for the most important moments while maintaining acceptable quality for less critical portions.
2Manufacturing precision
If video quality is maintained at high resolution, then visual fidelity is preserved, but processing time and computational resources increase
Solution Approach 1:
The system applies full-quality processing only to identified highlight segments rather than processing entire videos at full resolution. By detecting key moments and applying high-quality encoding only to those portions, the system achieves high visual fidelity where needed while significantly reducing overall processing time and computational resource consumption.
3Measurement precision
If automated classification is implemented to identify important moments, then relevant media items are extracted accurately, but system complexity increases
Solution Approach 1:
The system uses the video content itself to automatically identify important moments through analysis of motion patterns, scene transitions, and detected objects. The video data provides the information needed for classification without requiring external metadata or manual input, enabling accurate moment identification while keeping the system relatively simple and self-contained.
Data Source
AI summary
Techniques disclosed for managing video captured by an imaging device. Methods disclosed capture a video in response to a capture command received at the imaging device. Following a video capture, techniques for classifying the captured video based on feature(s) extracted therefrom, for marking the captured video based on the classification, and for generating a media item from the captured video according to the marking are disclosed. Accordingly, the captured video may be classified as representing a static event, and, as a result, a media item of a still image may be generated. Otherwise, the captured video may be classified as representing a dynamic event, and, as a result, a media item of a video may be generated.