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Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...

Relative Motion Analysis using Rotating Axes-Problem Solving

Relative Motion Analysis using Rotating Axes-Problem Solving

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Scaling

Scaling

In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...

Relative Motion Analysis using Rotating Axes - Acceleration

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Related Experiment Video

Updated: Mar 7, 2026

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters.

Soowoong Jeong¹, Guisik Kim², Sangkeun Lee³

¹Graduate School of Advanced Imaging Science, Multimedia, and Film, Chung-Ang University, Seoul 06974, Korea. imgrecog@gmail.com.

Sensors (Basel, Switzerland)

|March 1, 2017

Summary

This summary is machine-generated.

This study introduces a robust visual tracking method to handle scale variation and occlusion. The enhanced Kernelized Correlation Filter (KCF) tracker significantly improves object tracking performance in challenging scenarios.

Keywords:

adaptive learning rate computer vision correlation filter illumination variation multi-block method partial occlusion scale variation visual tracking

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Area of Science:

Computer Vision
Machine Learning

Background:

Visual tracking faces challenges with scale changes and occlusions.
Kernelized Correlation Filter (KCF) trackers offer speed and accuracy but struggle with scale variation and occlusion.

Purpose of the Study:

To develop a robust object tracking algorithm that effectively handles scale variation and occlusion.
To improve the performance of Kernelized Correlation Filter (KCF) trackers in challenging visual conditions.

Main Methods:

Implemented a scale space filter and multi-block scheme within a KCF tracker.
Developed an appearance update model to manage occlusion and deformation.
Utilized an adaptive update scheme for enhanced robustness.

Main Results:

The proposed tracker outperformed 29 state-of-the-art methods on 100 challenging sequences.
Achieved an 8% improvement on occlusion sequences and an 18% improvement on scale variation sequences compared to KCF.
Demonstrated superior robustness in handling scale variation and occlusion.

Conclusions:

The proposed tracker is a robust and effective solution for object tracking under scale variation and occlusion.
The integration of scale space filtering, multi-block schemes, and adaptive appearance models enhances tracking performance.