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

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

You might also read

Related Articles

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

Sort by

Same author

Sub-grain boundaries by cold-rolling copper nanoparticles for boosting alkaline HER.

Chemical communications (Cambridge, England)·2026

Same author

[Research on the changing trends of filter inlet pressure and extracorporeal circuit coagulation degree in patients with sepsis-induced coagulopathy undergoing continuous renal replacement therapy].

Zhonghua wei zhong bing ji jiu yi xue·2026

Same author

A Quantitative Efflux Ratio-Integrated Model for Passive Permeability Estimation from Caco-2 Assays.

Drug metabolism and bioanalysis·2026

Same author

Predicting Massive Transfusion Needs in Trauma Using Machine Learning: Systematic Review and Meta-Analysis.

Journal of trauma nursing : the official journal of the Society of Trauma Nurses·2026

Same author

Multisite Coadsorption of the *OOH Intermediate on NiFeOOH Hierarchical Nanosheet Arrays Boost Water Electro-Oxidation at Ultrahigh Current Densities.

ACS applied materials & interfaces·2026

Same author

Repression of EEF1D by KSHV RTA promotes viral lytic reactivation.

Journal of virology·2026

Search research articles

Related Experiment Video

Updated: Oct 2, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Pedestrian Detection with Multi-View Convolution Fusion Algorithm.

Yuhong Liu¹, Chunyan Han¹, Lin Zhang¹

¹School of Software, Northeastern University, Shenyang 110167, China.

Entropy (Basel, Switzerland)

|February 25, 2022

Summary

This summary is machine-generated.

This study introduces an improved multi-view pedestrian detection method for autonomous driving. The novel approach enhances accuracy and speed in crowded scenes, outperforming existing state-of-the-art models.

Keywords:

autonomous driving convolution fusion keypoints multiview pedestrian detection

More Related Videos

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

Related Experiment Videos

Last Updated: Oct 2, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

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

Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Single 2D image pedestrian detection struggles in crowded autonomous driving scenes.
Multi-view methods have improved pedestrian detection in challenging environments.

Purpose of the Study:

To develop a robust pedestrian detection system for autonomous driving in crowded and fuzzy conditions.
To enhance both accuracy and speed of pedestrian detection.

Main Methods:

A double-branch feature fusion structure with a lightweight first branch and a feature-extracting second branch.
Utilized expanded convolution to enlarge the receptive field.
Employed keypoint regression instead of whole object regression to improve speed, eliminating Non-Maximum Suppression (NMS) post-processing.

Main Results:

The proposed model achieves superior accuracy and speed compared to state-of-the-art methods on the Wildtrack and MultiviewX datasets.
The end-to-end learned model demonstrates robust performance even in densely populated scenarios.

Conclusions:

The developed pedestrian detection method offers significant practical value for autonomous driving systems.
The approach effectively addresses limitations of current methods in complex, crowded environments.