Automatic Video Highlight Production System
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Solution Overview
Problem
Existing methods for automatically producing video highlights from sports events are inefficient in identifying and segmenting important moments, as they rely heavily on audio signals and manual user input, lacking comprehensive analysis of video and player movements.
Innovation Solution
A method that receives synchronized audio and video from cameras positioned near a playing field, applies low-level processing to extract features, performs rough segmentation, and uses analytics algorithms, including machine learning, to identify and classify highlights based on pre-existing knowledge of the field and player movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If audio signals and manual user input are used for highlight identification, then the system is simpler to implement, but the accuracy and comprehensiveness of highlight detection deteriorates
Solution Approach 1:
The video processing system is divided into multiple independent modules: audio signal processing module, video frame analysis module, player movement tracking module, and highlight detection module. Each module processes specific aspects independently and their results are integrated, allowing the system to achieve comprehensive analysis without requiring complete redesign of a single complex system
Solution Approach 2:
The system integrates multiple detection functions into a unified platform that simultaneously analyzes audio signals, video frames, and player movements. This multi-functional approach allows the system to process diverse data types (audio, video, motion) through a single integrated architecture, improving both accuracy and implementation efficiency
2Measurement precision
If comprehensive analysis of video and player movements is performed, then the accuracy of highlight identification improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary processing of video frames and audio signals to extract key features before comprehensive analysis. Low-level processing extracts basic features from raw video and audio data, which are then used by higher-level analysis modules. This hierarchical approach reduces the complexity of subsequent comprehensive analysis by working with pre-processed feature data rather than raw data
Solution Approach 2:
The system transitions from analyzing individual video frames to tracking player movements across multiple frames, adding a temporal dimension to the analysis. By examining movement patterns and trajectories over time rather than static frames, the system achieves more accurate highlight detection while efficiently filtering out non-highlight segments through motion pattern recognition
3Use of energy by moving object
If manual user input is required for highlight selection, then the system requires less computational processing, but the productivity and efficiency of highlight production decreases
Solution Approach 1:
The system automatically detects and identifies highlights through integrated analysis of audio signals, video content, and player movements without requiring manual user input. The analytics algorithms process the extracted features and autonomously determine highlight segments, enabling the system to serve itself by eliminating the need for human operators to review and select highlights manually
4Device complexity
If audio signals are the primary basis for highlight identification, then the system is simpler to design, but the reliability of highlight detection deteriorates due to lack of visual context
Solution Approach 1:
The system merges audio signal analysis with video frame analysis and player movement tracking to create a unified highlight detection mechanism. By combining multiple data sources (audio, visual, motion) that complement each other, the system achieves reliable highlight detection where audio provides temporal cues and video/movement data provide visual context and confirmation
Data Source
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AI summary
Methods and systems are provided for automatically producing highlights videos from one or more video streams of a playing field. The video streams are captured from at least one camera, calibrated and raw inputs are obtained from audio, calibrated videos and actual event time. Features are then extracted from the calibrated raw inputs, segments are created, specific events are identified and highlights are determined and the highlights are outputted for consumption, considering diverse types of packages. Types of packages may be based on user preference. The calibrated video streams may be received and processed in real time, periodically.