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Relative Motion Analysis using Rotating Axes01:25

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 instrumental in...
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the drone...
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.
Here, in order to determine the magnitude of velocity and acceleration for point...
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

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.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

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.
Time differentiation is...
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

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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Related Experiment Video

Updated: Jul 7, 2026

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans
10:51

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval.

Ronan Fablet1, Patrick Bouthemy, Patrick Pérez

  • 1IRISA, Rennes, France. rfablet@irisa.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel video indexing and retrieval method using causal Gibbs models to interpret dynamic content without dense optic flow. This approach enables efficient query-by-example retrieval based on motion similarity.

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Related Experiment Videos

Last Updated: Jul 7, 2026

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans
10:51

Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans

Published on: January 15, 2018

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Image Processing

Background:

  • Content-based video analysis is crucial for efficient information retrieval.
  • Existing methods often rely on motion segmentation or dense optic flow, which can be computationally intensive and complex.
  • A need exists for robust video indexing and retrieval systems that can interpret dynamic content effectively.

Purpose of the Study:

  • To propose an original approach for content-based video indexing and retrieval.
  • To interpret the dynamic content of video shots globally, avoiding prior motion segmentation and dense optic flow.
  • To develop a statistical framework for query-by-example video retrieval based on motion similarity.

Main Methods:

  • Exploiting spatio-temporal distributions of local motion-related measurements derived from intensity function derivatives.
  • Representing these distributions using causal Gibbs models.
  • Compensating for dominant image motion to ensure independence from camera movement.
  • Utilizing a statistical modeling framework for computing conditional likelihoods for motion or activity classes.
  • Building a hierarchical binary tree structure for the video database based on motion content similarity.
  • Employing a similarity measure inspired by Kullback-Leibler divergence and the maximum a posteriori (MAP) criterion for retrieval.

Main Results:

  • Demonstrated the feasibility of computing conditional likelihoods for motion and activity classes.
  • Developed a general statistical framework for video indexing and retrieval with query-by-example.
  • Achieved promising results on various real image sequences.
  • Successfully built a hierarchical structure for video database retrieval based on motion similarity.

Conclusions:

  • The proposed method offers an effective way to index and retrieve video content based on dynamic motion interpretation.
  • The use of causal Gibbs models and motion similarity provides a robust framework for query-by-example retrieval.
  • The approach demonstrates potential for real-world applications in video analysis and information retrieval.