Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

738
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...
738
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.5K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.5K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

603
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...
603
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

235
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
235
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

575
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...
575
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

432
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...
432

You might also read

Related Articles

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

Sort by
Same author

The impact of the zero-waste city pilot policy on the synergistic reduction of CO₂ and air pollutant emissions: evidence from China.

Frontiers in public health·2026
Same author

From algorithms to clinical execution: A cross-validated knowledge atlas of AI-enabled precision care (2015-2025).

Digital health·2026
Same author

Pan-cancer prioritization of CCDC69 reveals an immune-enriched and therapeutically sensitive breast cancer phenotype.

Discover oncology·2026
Same author

Conformal phase-transition hydrogel interfaces for high fidelity electrophysiological sensing and data-driven inference.

Soft matter·2026
Same author

Moderate Ce doping enables outstanding oxygen evolution activity and stability in CoMn-LDH nanosheets.

Nanoscale·2026
Same author

Betaine induces ferroptotic stress by enhancing KEAP1-mediated NRF2 degradation in breast cancer cells.

Cellular signalling·2026

Related Experiment Video

Updated: Dec 6, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

9.4K

TSM: Temporal Shift Module for Efficient and Scalable Video Understanding on Edge Devices.

Ji Lin, Chuang Gan, Kuan Wang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 9, 2020
    PubMed
    Summary

    The Temporal Shift Module (TSM) enables efficient video understanding by shifting channels in 2D CNNs. This method achieves high accuracy and speed without extra computation, outperforming existing approaches.

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    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

    9.4K

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Video streaming's rapid growth necessitates efficient video understanding.
    • 2D CNNs lack temporal modeling, while 3D CNNs are computationally expensive.
    • Existing methods struggle to balance accuracy and computational cost.

    Purpose of the Study:

    • To introduce a generic and effective Temporal Shift Module (TSM) for efficient video understanding.
    • To enable temporal modeling in 2D Convolutional Neural Networks (CNNs) with zero computation and zero parameters.
    • To achieve high performance and efficiency in video recognition tasks.

    Main Methods:

    • Proposing the Temporal Shift Module (TSM) that shifts channels along the temporal dimension.
    • Integrating TSM into 2D CNN architectures to facilitate inter-frame information exchange.
    • Evaluating TSM on the Something-Something dataset and real-world devices for online video recognition.

    Main Results:

    • TSM achieved state-of-the-art performance, ranking first on the Something-Something leaderboard.
    • High efficiency demonstrated with 74fps and 29fps on Jetson Nano and Galaxy Note8, respectively.
    • Scalable training enabled, completing large-scale Kinetics training in 15 minutes on 1,536 GPUs.
    • TSM facilitated learning of action concepts, with emergent spatial-temporal action detectors.

    Conclusions:

    • TSM offers a highly efficient and performant solution for video understanding.
    • The module integrates seamlessly with 2D CNNs, enhancing temporal modeling capabilities.
    • TSM represents a significant advancement in efficient and accurate video analysis.