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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.
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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...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Relative Motion Analysis - Velocity01:24

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Relative Motion Analysis using Rotating Axes - Acceleration01:22

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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. 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...
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Real-time accumulative computation motion detectors.

Antonio Fernández-Caballero1, María Teresa López, José Carlos Castillo

  • 1Instituto de Investigación en Informática de Albacete, 02071-Albacete, Spain; E-Mails: mlopez@dsi.uclm.es (M.T.L.); josecarlos@dsi.uclm.es (J.C.C.).

Sensors (Basel, Switzerland)
|February 4, 2012
PubMed
Summary
This summary is machine-generated.

This study simplifies the accumulative computation (AC) method into a finite state machine for real-time motion detection. Hardware implementation on FPGA demonstrates promising performance for surveillance applications.

Keywords:
accumulative computationfinite state automatamotion detectionreal-time

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Neural networks are successful for problem-solving, while finite state machines are well-characterized computational models.
  • The relationship between neural networks and finite state machines has been a focus of research.
  • Real-time performance is crucial for many computational tasks, including motion detection.

Purpose of the Study:

  • To simplify the accumulative computation (AC) method by transforming it into a finite state machine.
  • To implement the AC method in hardware using FPGA for real-time motion detection.
  • To evaluate the performance of the AC motion detector in surveillance applications.

Main Methods:

  • Formal transformation of the general AC method into a finite state machine.
  • Hardware implementation of the AC module and an 8-AC motion detector on FPGA.
  • Case studies involving infrared-based people segmentation and color-based people tracking.

Main Results:

  • Successful simplification of the AC method into a finite state machine.
  • Promising performance results from the FPGA hardware implementation of the AC motion detector.
  • Demonstrated utility of AC motion detectors in surveillance scenarios.

Conclusions:

  • The finite state machine model enables real-time performance for the AC method.
  • FPGA implementation offers an efficient hardware solution for AC-based motion detection.
  • AC motion detectors are effective for surveillance tasks like people segmentation and tracking.