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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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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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Absolute Motion Analysis- General Plane Motion01:24

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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Related Experiment Video

Updated: Apr 12, 2026

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware
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Markerless human motion tracking using hierarchical multi-swarm cooperative particle swarm optimization.

Sanjay Saini1, Nordin Zakaria1, Dayang Rohaya Awang Rambli1

  • 1Computer and Information Science Department, Universiti Teknologi PETRONAS, Tronoh, Perak, Malaysia.

Plos One
|May 16, 2015
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Summary

This study introduces Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO) for markerless human motion tracking. The new method improves accuracy and self-recovery compared to existing Particle Swarm Optimization techniques.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Markerless full-body articulated human motion tracking presents a high-dimensional search space challenge.
  • Conventional Particle Swarm Optimization (PSO) methods often suffer from premature convergence and local optima, impacting tracking accuracy.

Purpose of the Study:

  • To develop an improved metaheuristic approach for accurate markerless human motion tracking.
  • To address the limitations of classical PSO in complex, high-dimensional optimization problems.

Main Methods:

  • Formulation of human motion tracking as a non-linear 34-dimensional function optimization problem.
  • Development and application of Hierarchical Multi-Swarm Cooperative Particle Swarm Optimization (H-MCPSO).
  • Utilization of silhouette and edge likelihoods within the fitness function to quantify model-image discrepancies.

Main Results:

  • H-MCPSO demonstrated superior performance compared to Annealed Particle Filter (APF) and Hierarchical Particle Swarm Optimization (HPSO) on the Brown and HumanEva-II datasets.
  • The proposed method achieved automatic initialization and robust self-recovery from temporary tracking failures.
  • Experimental results validate the effectiveness and reliability of the H-MCPSO approach.

Conclusions:

  • H-MCPSO offers a significant advancement in markerless human motion tracking accuracy and robustness.
  • The developed method effectively overcomes the limitations of traditional PSO algorithms in this domain.
  • The system's ability for automatic initialization and self-recovery enhances its practical applicability.