Motion Feature Extraction
Motion Feature Extraction - Related Videos
06:03
Motion-induced Blindness
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University One thing becomes very salient after basic exposure to the science of visual perception and sensation: what people see is a creation of the brain. As a result people may fail to see things, see things that are not there, or see things in a distorted way. To distinguish between physical reality and what people perceive, scientists use the term awareness to refer to what people perceive. To study awareness, vision scientists often...
11:41
Kinematics and Projectile Motion
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2023
Source: Ketron Mitchell-Wynne, PhD, Asantha Cooray, PhD, Department of Physics & Astronomy, School of Physical Sciences, University of California, Irvine, CA This experiment demonstrates the kinematics of motion in 1 and 2 dimensions. This lab will begin by studying motion in 1 dimension, under constant acceleration, by launching a projectile directly upward and measuring the maximum height reached. This lab will verify that the maximum height reached is consistent with the kinematic...
08:18
Visualizing Motion Patterns in Acupuncture Manipulation
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Cited by 6 •
2016
Here, we present a protocol for using the Acupuncture Manipulation Education System (AMES) in the training of acupuncture manipulation skills using phantom acupoints.
10:12
Newton's Laws of Motion
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2023
Source: Andrew Duffy, PhD, Department of Physics, Boston University, Boston, MA This experiment examines at various situations involving two interacting objects. First, the experiment examines the forces that two objects apply to one another while they collide. The objects are wheeled carts that have variable masses. The purpose of this experiment is to discover when the force the first cart exerts on the other is the same magnitude as the force the second cart exerts back on the first, as well...
05:42
Visual Search for Features and Conjunctions
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University How do people find objects in cluttered visual scenes? Think, for example, of looking for keys on a messy desk, finding the ripest-looking fruit at the grocery store, locating your car when you can’t quite remember where you parked it, or finding an old friend at an airport exit gate. Clearly, an understanding of visual perception is going to play a role in any answers, and more specifically, an understanding of visual attention...
FAQs
Noise reduction and tracking are important because raw movement signals can obscure the changes needed to calculate meaningful descriptors. Filtering reduces unwanted variation, while tracking supplies a time-ordered position record from which displacement, velocity, acceleration, trajectory, and direction can be derived. This sequence helps distinguish genuine motion from measurement artifacts before features enter engineering analysis or machine-learning models.
Each feature captures a different aspect of movement. Displacement describes positional change, while velocity and acceleration indicate how position changes over time and how that change itself varies. A trajectory represents the path followed, direction describes orientation of movement, and frequency characterizes repetition or periodic behavior. Combining these descriptors gives engineering systems several complementary ways to distinguish motion patterns.
Feature selection determines which characteristics of movement remain visible to an analysis system. Descriptors emphasizing speed or acceleration can support recognition of changing behavior, whereas trajectory or direction can distinguish different paths. Frequency can help represent repeated motion. Choosing features that match the engineering task can improve pattern classification, system responsiveness, and the detection of abnormal behavior.
A typical workflow begins by collecting video or sensor signals, followed by reducing noise in those measurements. Positions are then tracked over time, and descriptors such as displacement, velocity, acceleration, trajectory, direction, or frequency are calculated. The resulting feature data can be analyzed directly or supplied to machine-learning models for recognition, monitoring, or control tasks.
Extracted motion features support activity recognition, robotic control, machine monitoring, biomechanics, and human-machine interaction. In each case, movement is converted into measurable information that an engineering system can analyze or use for response. These features can help classify movement patterns, monitor machines for unusual behavior, improve interaction with users, and guide the design of safer, more efficient systems.
By making movement measurable, the technique can reveal abnormal behavior and provide signals for timely engineering decisions. A monitoring system may analyze changes in motion patterns, while a robotic or interactive system can use extracted descriptors to adjust its response. The same information can also inform system design by showing how motion relates to safety, efficiency, and expected operation.