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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 - Acceleration

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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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
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Related Experiment Video

Updated: Sep 4, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Published on: May 7, 2019

An Effective Video Transformer With Synchronized Spatiotemporal and Spatial Self-Attention for Action Recognition.

Saghir Alfasly, Charles K Chui, Qingtang Jiang

IEEE Transactions on Neural Networks and Learning Systems

|July 20, 2022

Summary

We introduce three techniques to enhance video understanding using video Transformers, improving efficiency and performance. Our novel methods, including synchronized spatiotemporal and spatial attention, achieve state-of-the-art results on benchmark datasets.

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Convolutional neural networks (CNNs) have dominated vision tasks, but vision Transformers (ViTs) now lead in image recognition.
Video Transformers lag behind image Transformers in research attention and efficiency due to parameter and training differences.

Purpose of the Study:

To propose and validate techniques for improving video understanding using Transformer architectures.
To enhance the efficiency and effectiveness of video Transformers.

Main Methods:

Introduced a synchronized spatiotemporal and spatial attention (SSTSA) scheme for better spatiotemporal feature representation.
Developed a motion spotlighting module to incorporate short-term motion into RGB inputs for a single-stream Transformer.
Implemented an intraclass frame interlacing method as a video augmentation technique.

Main Results:

The proposed video Transformer, incorporating SSTSA, motion spotlighting, and frame interlacing, demonstrated superior performance.
Achieved state-of-the-art results on the Kinetics400 and Something-Something-v2 datasets.

Conclusions:

The developed techniques significantly advance video understanding capabilities of Transformer models.
The proposed methods offer a more efficient and effective approach to video Transformer design.