Video Action Segmentation Using Multi-Modal Classifiers
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Solution Overview
Problem
Users often find it tedious to watch entire videos to find interesting parts, especially for longer videos like sports events or live streams, as they are not efficiently summarized or highlighted.
Innovation Solution
A system that uses audio, RGB, and motion classifiers to identify action segments in videos, generate confidence scores, and create video clips showcasing these actions, allowing users to view highlights without watching the entire video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users watch entire videos to find interesting parts, then they can ensure they don't miss any content, but it consumes excessive time and reduces user engagement
Solution Approach 1:
The patent segments videos into multiple short clips based on detected actions, transitions, and key moments. Each segment represents a meaningful unit of content that can be independently viewed, allowing users to quickly grasp video highlights without watching the entire video.
Solution Approach 2:
The system extracts and isolates key moments, actions, and interesting segments from the original video. By identifying and separating these important parts through action detection and transition analysis, the system creates a condensed representation that captures essential content while eliminating redundant portions.
2Productivity
If videos are summarized into short clips, then users can quickly view key moments, but the system complexity increases due to action detection and segment identification
Solution Approach 1:
The system performs preliminary action detection and segment identification during video upload or preprocessing. By detecting actions, transitions, and key moments before user viewing, the system prepares segmented clips in advance, enabling fast delivery and reducing real-time processing requirements.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes action detection models, transition detection algorithms, and segment selection logic. This intermediary system acts as a mediator between the original video and the user interface, automatically identifying and selecting important segments based on predefined criteria and machine learning models.
3Ease of operation
If the system processes and segments videos automatically, then user engagement improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial processing by focusing on detecting only the most critical elements (key actions, major transitions, significant moments) rather than analyzing every frame in detail. This selective approach reduces computational overhead while still generating meaningful segments that capture video highlights.
Solution Approach 2:
The patent employs periodic sampling and frame skipping in action detection, analyzing frames at intervals rather than continuously. This periodic processing reduces computational load while maintaining adequate detection accuracy, allowing the system to segment videos efficiently without requiring exhaustive frame-by-frame analysis.
Data Source
AI summary
A computer-implemented method includes receiving a video that includes multiple frames. The method further includes identifying a start time and an end time of each action in the video based on application of one or more of an audio classifier, an RGB classifier, and a motion classifier. The method further includes identifying video segments from the video that include frames between the start time and the end time for each action in the video. The method further includes generating a confidence score for each of the video segments based on a probability that a corresponding action corresponds to one or more of a set of predetermined actions. The method further includes selecting a subset of the video segments based on the confidence score for each of the video segments.