Video Frame Motion Comparison via Optical Flow Analysis
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Solution Overview
Problem
Existing motion capture and comparison techniques face challenges such as the need for affixed tracking markers, complex camera setups, and variations in accuracy due to lighting and obscuration, limiting their applicability and reliability.
Innovation Solution
A method that processes video frames to generate motion data frames using optical flow analysis, comparing these frames to a reference motion to calculate a similarity metric, allowing for accurate comparison without the need for tracking markers or complex camera setups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tracking markers are affixed to the subject, then motion tracking accuracy is improved, but the technique becomes limited to certain scenarios and requires markers to be visible throughout the sequence
Solution Approach 1:
The patent extracts the motion information from the visual appearance of the subject itself rather than relying on external tracking markers. By using computer vision algorithms to directly analyze the subject's visual features and motion patterns in the video frames, the system eliminates the need for affixed markers while maintaining motion tracking capability.
Solution Approach 2:
The patent creates a digital model or representation of the subject's motion by processing video frames through computer vision algorithms. This virtual model captures the subject's movement patterns without requiring physical markers, allowing the system to track motion in scenarios where markers would not be visible or practical.
2Measurement precision
If multiple cameras are used to record from multiple directions, then three-dimensional trajectory accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent divides the motion analysis into sequential processing of individual video frames rather than requiring simultaneous multi-camera recording. By analyzing each frame independently and tracking motion progression through the sequence, the system achieves three-dimensional trajectory information without the complexity of coordinating multiple cameras.
Solution Approach 2:
The patent replaces the mechanical system of multiple physical cameras with a computational approach using computer vision algorithms. The system processes standard video frames through image processing and pattern recognition to extract three-dimensional motion information, substituting computational complexity for hardware complexity.
3Measurement precision
If known techniques are used for motion capture, then motion data can be obtained, but accuracy varies due to lighting conditions, obscuration, and other factors
Solution Approach 1:
The patent employs dynamic adaptive algorithms that adjust to varying lighting conditions and obscuration levels. The computer vision system continuously adapts its parameter settings and processing methods based on the current visual conditions, maintaining consistent motion capture accuracy across different environmental scenarios.
Solution Approach 2:
The patent changes key processing parameters dynamically based on detected conditions such as lighting intensity and level of obscuration. By adjusting algorithmic parameters like contrast thresholds, feature detection sensitivity, and frame integration weights, the system maintains reliable motion capture accuracy across varying environmental conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables effective comparison of subject motion to a reference motion, providing a similarity metric that assesses performance and provides feedback, while being robust to variations in lighting and obscuration, thus improving accuracy and applicability.
Implementation Method 1
processes pairs of the received video frames to generate a sequence of motion data frames defining motion of the subject
Data Source
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AI summary
A method is disclosed of processing a sequence of video frames showing motion of a subject to compare the motion of the subject with a reference motion. The method comprises storing at least one reference motion data frame defining a reference motion, each reference motion data frame corresponding to respective first and second reference video frames in a sequence of video frames showing the reference motion and comprising a plurality of optical flow vectors, each optical flow vector corresponding to a respective area segment defined in the first reference video frame and a corresponding area segment defined in the second reference video frame and defining optical flow between the area segment defined in the first reference video frame and the area segment defined in the second reference video frame. The method further comprises receiving a sequence of video frames to be processed. The method further comprises processing at least one pair of the received video frames to generate a motion data frame defining motion of a subject between the pair of received video frames. Each pair of received video frames that is processed is processed by, for each area segment of the reference video frames, determining a corresponding area segment in a first video frame of the pair and a corresponding area segment in a second video frame of the pair. Each of the pairs of received video frames is further processed by, for each determined pair of corresponding area segments, comparing the area segments and generating an optical flow vector defining optical flow between the area segments. Each of the pairs of received video frames is further processed by generating a motion data frame for the pair of received video frames, the motion data frame comprising the optical flow vectors generated for the determined pairs of corresponding area segments. The method further comprises comparing the at least one reference motion data frame defining the reference motion to the at least one generated motion data frames defining the motion of the subject and generating a similarity metric for the motion of the subject and the reference motion.