Video Processing System for Real-Time Highlight Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for automatic detection of highlights in soccer videos, such as those by Assfalg et al, face computational burdens and delays due to the use of blob clustering, which can prevent real-time response in video processing systems.
Innovation Solution
A video processing system that includes an image segmentation module, a segment parameter extractor, a classifier, and a detector, which processes images from multiple cameras to dynamically select the most relevant camera feed based on event detection, reducing processing load by using multiplexers and image memories to identify and trigger video segments efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blob clustering is used to detect player positions and event cues, then detection accuracy is improved, but computational burden and processing time increase significantly
Solution Approach 1:
The patent extracts and processes only the most relevant visual features (player positions, team colors, jersey patterns) rather than performing complete blob clustering on all images. This selective feature extraction maintains detection accuracy while significantly reducing computational complexity and processing time.
Solution Approach 2:
The system performs preliminary processing of video frames to identify key events and segments before detailed analysis. By pre-identifying potential highlight moments through simplified criteria, the system avoids applying computationally intensive blob clustering to all frames, thus reducing overall computational burden while maintaining detection precision.
2Reliability
If blob clustering is performed on many images to avoid missing cues, then detection completeness is improved, but real-time response capability deteriorates
Solution Approach 1:
The patent implements periodic sampling and processing of video frames at strategically selected intervals rather than continuous analysis of every frame. This periodic approach ensures that key events are captured reliably while maintaining real-time response capability by processing only a subset of frames with simplified algorithms.
Solution Approach 2:
The system applies partial processing to most frames and excessive (detailed) processing only to selected frames where events are likely to occur. This selective application of computational resources ensures detection completeness for all frames while maintaining real-time performance by limiting intensive processing to critical moments only.
Data Source
Figure 1~2
Figure 3
AI summary
The video processing system provides for automatic selection of video sequences showing goals in a video stream from cameras directed at a playing field. Classification is applied to image content from different foreground positions in an image. The set of classes comprising a first and second class corresponding players of different teams. Medians or averages of the image positions of the local image content assigned to the first and second class are determined. A condition is tested whether the results are on mutually opposite sides of a reference position and a distance between collected positions or distances between the reference position and the first and second collected positions exceed a threshold value. When the test shows that the condition is met, a preceding video sequence is selected, for example for display.