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Relative Motion Analysis using Rotating Axes01:25

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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.
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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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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.
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Related Experiment Video

Updated: Sep 26, 2025

Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses
05:26

Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses

Published on: April 12, 2024

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Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator.

Hosik Choi1, Byungmun Kang1, DaeEun Kim1

  • 1School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study presents a novel sparse optical flow algorithm for efficient moving object detection and tracking. The method enhances accuracy and speed, even with occlusions and varying conditions.

Keywords:
moving object trackingmoving windowoptical flowtarget estimator

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Conventional moving object detection methods suffer from high computational costs, noise sensitivity, and target disappearance due to occlusions.
  • Existing techniques struggle with real-world complexities like varied illumination and dynamic environments.

Purpose of the Study:

  • To introduce a new moving object detection and tracking algorithm utilizing sparse optical flow.
  • To address limitations of conventional methods by reducing computation time, enhancing noise reduction, and improving target estimation efficiency.

Main Methods:

  • Developed a sparse optical flow-based algorithm incorporating a moving window detector for feature point selection based on location history.
  • Implemented a memory-based estimator to retain corner feature locations, enabling tracking of obscured targets.
  • Evaluated the algorithm on an embedded platform (Raspberry Pi 4) under diverse real-world conditions.

Main Results:

  • The proposed algorithm significantly improves moving object detection performance.
  • Achieved high frames per second (FPS) and enhanced accuracy compared to traditional optical flow, Haar-like, and HOG methods.
  • Demonstrated effective tracking of targets even when obscured by obstacles.

Conclusions:

  • The novel sparse optical flow algorithm offers a robust and efficient solution for moving object detection and tracking.
  • The method shows superior performance in challenging environments with varying illumination, multiple objects, and occlusions.
  • The developed approach is suitable for real-time applications on embedded systems.