Image Sequence Segmentation With Kinematic Motion-State Filtering
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Solution Overview
Problem
Traditional methods for performance analysis in professional sports rely on artificial observation of motion video and artificial post-data processing, lacking high-precision and real-time capabilities for accurate analysis of technical details and sports strategy adjustment.
Innovation Solution
A method for segmenting an image sequence by determining a motion state of a target object, updating motion states within a sliding window to comply with kinematics rules, and identifying segmentation points for precise image segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional artificial observation and post-data processing methods are used, then device complexity is reduced, but measurement precision and real-time performance deteriorate
Solution Approach 1:
The patent replaces traditional mechanical observation and manual data processing with an automated computer vision system that uses image sequence analysis and motion state detection algorithms to automatically identify and analyze athlete movements, achieving high-precision real-time measurement without manual intervention
Solution Approach 2:
The system enables self-service by automatically processing image sequences through motion detection algorithms that independently identify motion states, determine segmentation points, and generate analysis results without requiring external manual data processing or expert observation
2Measurement precision
If motion state detection is performed on every frame, then measurement precision improves, but productivity decreases due to computational load
Solution Approach 1:
The patent segments the image sequence into multiple frames and processes them in batches using a sliding window approach, determining motion states for groups of frames rather than individually processing each frame, which reduces computational overhead while maintaining detection precision through cumulative motion state updates
Solution Approach 2:
The system implements periodic action by updating motion states at regular intervals through the sliding window mechanism, processing a fixed number of frames before updating the motion state, which creates a rhythmic processing pattern that balances precision requirements with real-time performance constraints
3Measurement precision
If short-term fluctuations in motion data are not mitigated, then processing speed increases, but measurement precision deteriorates due to noisy data
Solution Approach 1:
The patent applies preliminary action by accumulating motion states from multiple frames before making a final determination, using the sliding window to gather preliminary motion data that is then synthesized into a reliable motion state classification, which filters out short-term fluctuations before the final measurement is recorded
Solution Approach 2:
The system implements feedback by continuously updating motion states based on accumulated frame data and using the determined motion state to guide subsequent processing, where the output of one processing cycle informs the next, creating a feedback loop that progressively refines the measurement and eliminates noise
Data Source
AI summary
A method for segmenting an image sequence is provided. In the method, a motion state of a target object in images is determined to obtain a motion state sequence based on the images in the image sequence; a target motion state among the multiple motion states within a sliding window of the motion state sequence is updated to obtain an updated motion state complying with a kinematics rule of the target object; a segmentation point corresponding to a motion process of the target object in the image sequence is determined based on the updated motion state; and the image sequence is segmented according to the segmentation point.