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-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

A functionally guided fusion Vision Transformer for predicting IDH status in gliomas: a multicenter study with external validation and incomplete multimodal evaluation.

Radiologie (Heidelberg, Germany)·2026

Same author

Booster vaccine reduces BCG-primed mice's protection against primary Mycobacterium tuberculosis infection by raising IL-10 levels.

Vaccine·2026

Same author

Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy.

Neuroinformatics·2026

Same author

SIB1-SEC23A undergo ER to chloroplast relocalization to mediate immunity in Arabidopsis thaliana.

Journal of integrative plant biology·2026

Same author

Cold plasma-modified goat milk casein/Chinese yam polysaccharide/chitosan composite films: Structural, functional, and preservative properties for fresh pork.

Food chemistry·2026

Same author

Autoantibodies Targeting Complement Regulating Factors Induced C3 Glomerulopathy Resambling C4 Dense Deposit Disease: A Case Report.

Nephrology (Carlton, Vic.)·2026

Same journal

In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors.

SpringerPlus·2017

Same journal

Erratum to: Implication of Paris Agreement in the context of long-term climate mitigation goals.

SpringerPlus·2017

Same journal

Erratum to: Associations between adherence, depressive symptoms and health-related quality of life in young adults with cystic fibrosis.

SpringerPlus·2017

Same journal

Erratum to: Numerical method to compute acoustic scattering effect of a moving source.

SpringerPlus·2017

Same journal

Identifying appropriate protected areas for endangered fern species under climate change.

SpringerPlus·2017

Same journal

An Algorithm to detect balancing of iterated line sigraph.

SpringerPlus·2017

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 16, 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

Multi-surface analysis for human action recognition in video.

Hong-Bo Zhang¹, Qing Lei¹, Bi-Neng Zhong¹

¹Department of Computer Science and Technology, Huaqiao University, Fujian, China.

|August 19, 2016

Summary

This summary is machine-generated.

This study introduces a new multi-surface video analysis for human action recognition. The novel three-surface motion feature (3SMF) improves prior probability estimation over traditional methods.

Keywords:

Human action recognition Multi-view video analysis Probability inference Three surfaces motion feature

More Related Videos

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Related Experiment Videos

Last Updated: Mar 16, 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

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Current human action recognition primarily relies on single-view or multi-camera video data.
Existing methods often utilize uniform distributions for prior probability estimation, potentially limiting performance.

Purpose of the Study:

To propose a novel multi-surface video analysis strategy for enhanced human action recognition.
To introduce a new feature representation called three-surface motion feature (3SMF) for improved action recognition.

Main Methods:

The proposed method extracts spatio-temporal interest features and a novel three-surface motion feature (3SMF).
3SMF is derived from motion history images across three distinct video surfaces: horizontal-vertical, horizontal-time, and vertical-time.
Prior probability is estimated using 3SMF, departing from uniform distribution assumptions.

Main Results:

The 3SMF approach demonstrates effectiveness in capturing motion dynamics across different video surfaces.
Probability inference is used to model the relationship between video features and action categories.
Experimental results show competitive performance against state-of-the-art action recognition benchmarks.

Conclusions:

The proposed multi-surface video analysis strategy and 3SMF offer a promising advancement in human action recognition.
Estimating prior probability with 3SMF enhances the discriminative power of the model.
This approach provides a robust framework for bridging feature descriptors and action categories in video analysis.