Streaming Clip Generation Using ML Event and Boundary Detection
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Solution Overview
Problem
Existing technologies face challenges in generating video clips based on real-time video data, particularly for live events like sporting events, as it is difficult to identify important events and temporal boundaries within the video stream.
Innovation Solution
A clip generation service utilizing machine-learning models to analyze video frames, determining scores for event likelihood, temporal boundaries, and redundancy, to generate video clips by identifying initial and terminating boundaries and concatenating relevant frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video clips are generated manually from live video data, then clip accuracy and quality can be ensured, but the process is too slow and cannot keep up with real-time streaming requirements
Solution Approach 1:
The patent replaces manual mechanical analysis of video content with machine-learning models that automatically analyze video frames to identify events and temporal boundaries. The system uses trained models to detect significant moments, determine clip start and end points, and generate clips without human intervention, thereby maintaining accuracy while dramatically increasing generation speed to match real-time streaming requirements
Solution Approach 2:
The patent implements preliminary action by pre-training machine-learning models on large datasets of video content before deployment. The models are预先 trained to recognize patterns, events, and temporal boundaries, enabling them to quickly and accurately generate clips from live video data without requiring real-time human expertise. This preliminary preparation allows the system to operate autonomously at high speed while maintaining consistent quality
2Productivity
If automated clip generation is implemented without machine-learning models, then processing speed increases, but the ability to accurately identify important events and temporal boundaries deteriorates
Solution Approach 1:
The patent replaces simple automated processing with sophisticated machine-learning models that have been trained to recognize significant events and temporal boundaries in video content. These models analyze video frames to identify important moments, determine appropriate clip boundaries, and generate meaningful clips automatically, combining high-speed processing with accurate event identification without requiring human intervention
Solution Approach 2:
The patent utilizes parameter changes by adjusting various model parameters and thresholds to optimize both speed and accuracy. The system dynamically adjusts parameters such as confidence thresholds, time windows for event detection, and clip duration parameters based on the specific video content and streaming requirements, enabling the automated system to maintain high accuracy while operating at maximum processing speed
3Reliability
If all video frames are processed and stored for potential clip generation, then no important moments are missed, but storage requirements and processing overhead increase significantly
Solution Approach 1:
The patent applies the extraction principle by using machine-learning models to identify and extract only the most significant video frames and sequences that contain important events. Instead of storing all video frames, the system selectively extracts frames that meet certain criteria for event significance, temporal boundary relevance, and clip composition quality, thereby reducing storage requirements while ensuring all important moments are captured
Solution Approach 2:
The patent implements segmentation by dividing the video stream into smaller segments or frames and processing them individually through the machine-learning model. The system analyzes each frame or small group of frames to determine event significance and temporal boundaries, generating clips only from segments that contain important content. This segmented approach reduces the overall data volume that needs to be stored and processed while maintaining complete event capture
Data Source
AI summary
A clip generation service can be used to generate streaming video clips. A video segment having video frames can be received from a video stream. Machine-learning models can be executed to determine likelihoods of each of the video frames representing (i) an event, (ii) potential temporal boundaries, and (iii) redundancies with respect to other video frames. A clip generation service can determine a video clip based at least in part on the determined likelihoods. The video clip can include (i) a first video frame being an initial temporal boundary of the video clip, (ii) a second video frame being a terminating temporal boundary of the video clip, and (iii) a third video frame being temporally between the first video frame and the second video frame. The third video frame may represent an event detected by the machine-learning models. Access to the video clip can be facilitated upon request.