how to measure the velocity of a runner based on computing vision

Introduction

Measuring the velocity of a runner using computer vision involves capturing and analyzing image sequences to determine motion. This process can be complex due to the need for accurate and efficient algorithms that can handle various challenges such as image blur, feature detection, and computational constraints.

Key Insights

Hierarchical and Gradient-Based Methods:
- Hierarchical frameworks use large-scale intensity information to obtain rough motion estimates, which are refined using smaller-scale data. These methods provide displacement vectors for pixels and use smoothness constraints to improve accuracy.
- Gradient-based algorithms, consistent with hierarchical frameworks, are also effective in measuring image velocity.
High-Speed Vision Techniques:
- High-speed pose and velocity measurement can be achieved by increasing information density rather than data rate transmission. This involves using a rotary sequential acquisition of selected regions of interest (ROI) to provide space-time data, which can be used to measure both pose and velocity.
Ego-Motion Computation:
- Single-camera systems can compute velocity by detecting and using road lane markings as features. This method reduces cumulative errors inherent in odometry-based systems and is particularly useful in robotics and intelligent vehicles .
Motion Blur Utilization:
- Motion blur in images can be exploited to measure angular velocity. By analyzing the dark parallel lines in the spectrum of a translation-blur image, the angular velocity can be determined.
Vision Detectors with Reduced Computing Time:
- Vision detectors using linear arrays of imaging charge-coupled devices (CCDs) can simplify motion detection and reduce computation time. This method uniquely determines the magnitude and direction of velocity for moving objects.
Pedestrian Speed Estimation:
- Fixed cameras can be used to estimate pedestrian velocity by converting image coordinates to real-world coordinates. This involves preprocessing, identification, tracking, and speed estimation phases.
Neural Gradient Models:
- Neural gradient models can measure image velocity by approximating gradient schemes through networks of neurons. These models can measure the speed of an object independently of its structural properties.
Optical Flow Algorithms:
- Optical flow algorithms can be integrated into control systems for traction electric drives to estimate linear velocity. This method is effective even on slippery surfaces and at low speeds.
Over-Sampled Single-Bit Representation:
- Velocity estimation can be achieved using an over-sampled single-bit representation, which simplifies correlation computations and provides reasonable velocity estimates with simple logical circuits.

Conclusion

Measuring the velocity of a runner using computer vision can be approached through various methods, including hierarchical frameworks, high-speed vision techniques, ego-motion computation, motion blur analysis, and optical flow algorithms. Each method has its strengths, such as reducing computational requirements, improving accuracy, or handling specific challenges like image blur and feature detection. Combining these insights can lead to the development of robust and efficient systems for velocity measurement in dynamic environments.

How to measure the velocity of a runner based on computing vision

Summary

Introduction

Key Insights

Conclusion

Sources and full results

A computational framework and an algorithm for the measurement of visual motion

Our hierarchical computational framework and algorithm effectively measure visual motion from digitized image sequences, providing dense displacement fields for pixels and a direction-dependent confidence measure.

High-speed pose and velocity measurement from vision

This paper presents a novel method for high-speed pose and velocity measurement from visual sensors, using rotary sequential acquisition of selected regions of interest (ROI) to increase information density and overcome the bottleneck phenomenon.

Ego-Motion Computing for Vehicle Velocity Estimation

This paper presents a reliable method for measuring vehicle velocity using a single camera onboard an automobile, using egomotion computation and road lane markings for accurate estimation.

A computer vision method for measuring angular velocity

This computer vision method, based on image motion blur, effectively measures angular velocity in images, proving its feasibility in estimating angular displacement and rotary speed.

Model of a fast vision detector for determination of the velocity of a moving object

The proposed vision detector model effectively measures the velocity of moving objects with reduced computing time, simplifying motion detection and reducing memory and computation time.

Vision-based Ego-motion Computing for Intelligent Vehicles

This paper presents a reliable method for computing vehicle velocity using a single camera onboard a road vehicle, using computer vision and road lane markings for accurate estimation.

Estimating Speeds of Pedestrians in Real-World Using Computer Vision

This paper proposes a novel computer vision-based automatic system for estimating pedestrian speeds in real-world traffic systems using a fixed camera, introducing a preprocessing phase, identification and tracking phases, and two different speed estimation techniques.

Neural gradient models for the measurement of image velocity

Neural gradient models can accurately measure image velocity, independent of object or pattern structural properties.

MEASUREMENT OF LINEAR VELOCITY USING A MOBILE ROBOTIC PLATFORM WITH COMPUTER VISION

Integrating computer vision algorithms into rail transport traction electric drives can improve power efficiency and prevent wheel slip, offering a promising alternative to conventional sensors.

Using over-sampled single-bit representation for velocity estimation in vision systems

The proposed over-sampled single-bit representation approach provides reasonable velocity estimates in vision systems, with potential advantages of reduced computational requirements and simple logical circuits.

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