How to measure the velocity of a runner based on computing vision
Pro Research Analysisby 
Searched over 200M research papers
Consensus Meter
Measuring the Velocity of a Runner Using Computer Vision
Introduction to Velocity Measurement in Computer Vision
Measuring the velocity of a runner using computer vision involves capturing and analyzing visual data to determine the speed and direction of movement. This process leverages various algorithms and techniques to ensure accuracy and adaptability under different conditions.
Monocular Vision and Kalman Filter Integration
Square-Root Cubature Kalman Filter (SRCKF)
One effective method for velocity measurement is the integration of monocular vision with the square-root cubature Kalman filter (SRCKF). This approach addresses the limitations of traditional optical flow algorithms, such as the Lucas-Kanade (LK) method, which struggles with low computational accuracy and poor adaptability under uneven illumination. By combining the LK optical flow with optimized oriented FAST and rotated BRIEF (ORB) algorithms, the SRCKF algorithm enhances the accuracy of velocity measurements and reduces sensitivity to lighting variations.
Stereo Vision Systems
Low-Cost Single Camera-Based Stereo Vision
Another approach involves using a low-cost, single camera-based stereo vision system. This system mimics human stereo vision by capturing slight differences between two images taken from different positions, known as disparity. This disparity is crucial for determining the distance and relative velocity of a moving object. The simplicity and cost-effectiveness of this system make it a viable option for measuring the position and relative velocity of a runner with desirable accuracy.
Image Motion Blur for Angular Velocity
Motion Blur Analysis
For measuring angular velocity, a method based on image motion blur can be employed. Motion blur, often considered a hindrance, can actually reveal vital motion information. By analyzing the trade-off between motion blur in spatial and frequency domains, the angular velocity can be determined. This involves extracting information from dark parallel lines in the spectrum of a translation-blur image, which are related to displacement during exposure. Applying polar transformation to rotation-blur images reduces rotation to translation, allowing for accurate angular velocity measurement.
Computational Neuroscience Insights
Spatiotemporal-Energy Models
Insights from computational neuroscience, particularly studies on the primate visual system, have contributed to understanding velocity computation. Spatiotemporal-energy models have been successful in representing local-velocity detection, although local-velocity measurements often differ from the overall object velocity. Certain cells in the middle temporal area combine local-velocity estimates to compute the overall pattern velocity, suggesting that simple operations in the spatiotemporal frequency domain are involved.
Hierarchical Computational Framework
Dense Displacement Fields
A robust method for measuring visual motion involves a hierarchical computational framework. This framework separates image intensity information by scale, using large-scale intensity for rough motion estimates and refining them with smaller-scale intensity information. The resulting displacement (or velocity) vectors for pixels are accompanied by a direction-dependent confidence measure, ensuring smooth propagation of high-confidence measurements to neighboring areas. This method is effective for determining dense displacement fields from image pairs, providing accurate velocity measurements.
Conclusion
Measuring the velocity of a runner using computer vision can be achieved through various methods, each with its own strengths. Integrating monocular vision with the SRCKF algorithm, employing low-cost stereo vision systems, analyzing motion blur for angular velocity, leveraging insights from computational neuroscience, and using hierarchical computational frameworks all contribute to accurate and reliable velocity measurements. These techniques ensure adaptability and precision, even under challenging conditions.
Sources and full results
Most relevant research papers on this topic