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Relative Motion Analysis using Rotating Axes01:25

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.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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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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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
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Relative Motion Analysis using Rotating Axes - Acceleration01:22

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Relative Motion Analysis - Acceleration01:10

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A slider-crank mechanism 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. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
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Improved Camshift object tracking algorithm in occluded scenes based on AKAZE and Kalman.

Lili Pei1, He Zhang2, Bo Yang3

  • 1School of Information Engineering, Chang'an University, Xi'an, 710064 Shaanxi China.

Multimedia Tools and Applications
|October 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Camshift object tracking algorithm using AKAZE feature matching and Kalman filtering. The enhanced algorithm successfully tracks objects during full occlusion, improving real-time performance.

Keywords:
AKAZE algorithmCamshift algorithmFeature matchingKalman filteringObject trackingVideo processing

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Area of Science:

  • Computer Vision
  • Object Tracking
  • Machine Learning

Background:

  • Traditional Camshift tracking struggles with object occlusion and similar background hues.
  • Robust object tracking is crucial for various applications like surveillance and autonomous systems.

Purpose of the Study:

  • To develop an improved Camshift object tracking algorithm that overcomes limitations of occlusion and background interference.
  • To enhance the real-time performance and accuracy of object tracking systems.

Main Methods:

  • Proposed an improved Camshift algorithm integrating Accelerated-KAZE (AKAZE) feature matching and Kalman filtering.
  • Implemented a scene-judgment threshold to switch between Camshift and Kalman tracking.
  • Utilized AKAZE for feature point matching and Kalman filtering for position prediction.

Main Results:

  • The improved algorithm demonstrated continuous tracking capability even during full object occlusion.
  • Achieved an approximate 20% increase in effective recognition frame rate.
  • Maintained a single-frame image processing time under 35 ms, meeting real-time requirements.

Conclusions:

  • The combined AKAZE and Kalman filtering approach significantly enhances Camshift's robustness and real-time tracking capabilities.
  • This improved algorithm offers a viable solution for challenging object tracking scenarios.
  • The method shows potential for practical applications requiring reliable and efficient visual tracking.