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Relative Motion Analysis using Rotating Axes

Relative Motion Analysis using Rotating Axes

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

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

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Related Experiment Video

Updated: Mar 6, 2026

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

Published on: August 22, 2025

Fast Pixelwise Adaptive Visual Tracking of Non-Rigid Objects.

Stefan Duffner, Christophe Garcia

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 10, 2017

Summary

This summary is machine-generated.

This study introduces a novel real-time object tracking algorithm for videos. The method excels in unconstrained environments, accurately tracking objects with significant appearance and shape changes.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Real-time object tracking in unconstrained video environments remains a significant challenge.
Existing methods often struggle with objects undergoing deformations and appearance variations.

Purpose of the Study:

To develop a robust and efficient algorithm for real-time single-object tracking in videos.
To address limitations of current tracking methods in handling object deformations and appearance changes.

Main Methods:

A novel algorithm combining a generalized Hough transform-based detector and a probabilistic segmentation method.
Co-training approach where detector and segmentation components adapt each other at the pixel level.
Incorporation of an adaptive shape model and a probabilistic scale update method.

Main Results:

The algorithm demonstrates superior performance on challenging benchmarks compared to state-of-the-art methods.
Effective tracking of objects with rigid and non-rigid deformations and significant appearance variations.
Achieved extremely fast tracking speeds due to an efficient implementation.

Conclusions:

The proposed algorithm offers a significant advancement in real-time single-object tracking.
Its robustness and efficiency make it suitable for diverse unconstrained video applications.
The co-training and adaptive modeling components are key to its high performance.