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

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Difference from Background: Limit of Detection

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

You might also read

Related Articles

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

Sort by

Same author

Staged Microbial Consortium Valorizes Cattle Manure Wastewater and Promotes Maize Growth.

Journal of applied microbiology·2026

Same author

Retraction notice to Analysis of the application value of multi-slice spiral CT and multimodal MRI in screening patients with bipolar disorder [Psychiatry Research: Neuroimaging 358 (2026) 112178].

Psychiatry research. Neuroimaging·2026

Same author

A pH-responsive miR-146a-loaded Gel/MSN nanocarrier enhances anti-inflammatory efficacy in allergic rhinitis through pyroptosis suppression.

Journal of translational medicine·2026

Same author

MamWorld for open-world object detection with state space modeling and cross-modal fusion.

Scientific reports·2026

Same author

Unsupervised radiomics-driven endotyping of chronic rhinosinusitis with nasal polyps: a multimodal characterization of CT imaging, clinical phenotypes, and proteomic profiling.

BMC medical imaging·2026

Same author

Corrigendum to ''Analysis of the Application Value of Multi-slice Spiral CT and Multimodal MRI in Screening Patients with Bipolar Disorder'' Psychiatry research. Neuroimaging, 358, 112178.

Psychiatry research. Neuroimaging·2026

Search research articles

Related Experiment Video

Updated: May 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Spatiotemporal uncertainty guided non maximum suppression for video event detection.

Fengqian Pang¹, Chunyue Lei², Yunjian He²

¹School of Information Science and Technology, North China University of Technology, Beijing, 100144, China. fqpang@ncut.edu.cn.

Scientific Reports

|April 8, 2025

Summary

This study introduces a novel neural network for Video Event Detection (VED) that estimates spatial and temporal uncertainty. This uncertainty guides Non-Maximum Suppression (NMS), improving detection accuracy in computer vision applications.

More Related Videos

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

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Related Experiment Videos

Last Updated: May 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

Area of Science:

Computer Vision
Artificial Intelligence

Background:

Video Event Detection (VED) is crucial for applications like autonomous driving and surveillance.
Existing VED methods primarily focus on network architectures, neglecting uncertainty estimation.
Uncertainty estimation is vital for reliable decision-making in VED systems.

Purpose of the Study:

To propose an end-to-end VED neural network incorporating spatial and temporal uncertainty.
To develop a novel Non-Maximum Suppression (NMS) method guided by estimated uncertainty, termed Spatio-Temporal Uncertainty guided NMS (STU-NMS).

Main Methods:

Developed an end-to-end neural network for VED.
Integrated spatial and temporal uncertainty estimation into the VED model.
Proposed STU-NMS, which utilizes estimated uncertainty to refine detection outputs.

Main Results:

The proposed method incorporating Spatio-Temporal Uncertainty (STU) integration outperforms existing techniques that do not model uncertainty.
STU-NMS demonstrated improved detection performance on benchmark datasets (J-HMDB-21, UCF101-24, AVA).

Conclusions:

Incorporating spatial and temporal uncertainty significantly enhances Video Event Detection.
The proposed STU-NMS method offers a superior approach to VED by leveraging uncertainty for improved accuracy.