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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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.
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Relative Motion Analysis using Rotating Axes

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Related Experiment Video

Updated: May 29, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Determining three-dimensional motion and structure from optical flow generated by several moving objects.

G Adiv1

  • 1Department of Computer and Information Science, University of Massachusetts, Amherst, MA 01003.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study introduces a novel method for analyzing sparse, noisy optical flow fields from moving sensors and multiple objects. The approach segments flow data to identify rigid object motion and estimate 3D structure.

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Last Updated: May 29, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging
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Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
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Area of Science:

  • Computer Vision
  • Robotics
  • Motion Estimation

Background:

  • Interpreting optical flow fields from sensors in dynamic environments with multiple independently moving objects is challenging.
  • Existing methods often struggle with sparse, noisy, or partially incorrect flow data.

Purpose of the Study:

  • To develop a robust approach for the interpretation of challenging optical flow fields.
  • To enable the recovery of three-dimensional (3-D) motion parameters and relative environmental depth.

Main Methods:

  • The approach involves two main stages: partitioning the flow field into segments consistent with rigid planar motion, and grouping these segments to hypothesize single object motion.
  • Hypotheses are tested by recovering 3-D motion parameters compatible with grouped segments.

Main Results:

  • The method successfully segments and interprets optical flow fields, even when they are sparse, noisy, and partially incorrect.
  • Experiments with real and simulated data demonstrate the effectiveness of the proposed approach in recovering motion and depth.

Conclusions:

  • The presented method offers a robust solution for optical flow interpretation in complex dynamic scenes.
  • It facilitates the estimation of object motion and environmental structure from challenging sensor data.