Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Absolute Motion Analysis- General Plane Motion

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...

Relative Motion Analysis using Rotating Axes

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...

Relative Motion Analysis using Rotating Axes-Problem Solving

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Treatment outcomes, symptoms of anxiety and depression, and satisfaction with life after orthognathic surgery.

Frontiers in oral health·2026

Same author

NeuralFeels with neural fields: Visuotactile perception for in-hand manipulation.

Science robotics·2024

Same author

Author Correction: Microridge-like structures anchor motile cilia.

Nature communications·2024

Same author

Internal world models in humans, animals, and AI.

Neuron·2024

Same author

Ego4D: Around the World in 3,600 Hours of Egocentric Video.

IEEE transactions on pattern analysis and machine intelligence·2024

Same author

Internal world models in humans, animals, and AI.

Neuron·2024

Search research articles

Related Experiment Video

Updated: Apr 4, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Segmentation of Moving Objects by Long Term Video Analysis.

Peter Ochs, Jitendra Malik, Thomas Brox

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 10, 2015

Summary

This summary is machine-generated.

Analyzing motion over extended time windows improves unsupervised object grouping. This method yields temporally consistent results, reducing post-processing needs for motion segmentation.

More Related Videos

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

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

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

Published on: January 15, 2018

Related Experiment Videos

Last Updated: Apr 4, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Published on: April 8, 2019

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

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

Published on: January 15, 2018

Area of Science:

Computer Vision
Machine Learning

Background:

Unsupervised object grouping is crucial for scene understanding.
Classical optical flow methods struggle with short-term motion variations, hindering accurate object separation.

Purpose of the Study:

To demonstrate that analyzing motion over larger time windows enhances unsupervised object-level grouping.
To introduce a novel paradigm for motion segmentation that leverages long-term trajectories and color information.

Main Methods:

Utilizing point trajectories spanning hundreds of frames for robust motion analysis.
Implementing a paradigm starting with semi-dense motion cues, followed by textureless area completion using color.
Introducing the Freiburg-Berkeley Motion Segmentation (FBMS) dataset.

Main Results:

Longer time window analysis significantly reduces susceptibility to short-term variations.
Achieving temporally consistent object groupings across entire video shots.
The FBMS dataset provides a benchmark for evaluating motion segmentation algorithms.

Conclusions:

Exploiting motion over larger time windows is highly effective for unsupervised object grouping.
The proposed method offers temporally consistent segmentations, simplifying downstream processing.
The FBMS dataset facilitates advancements in motion segmentation research.