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 Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

407
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...
407
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

471
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...
471
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.4K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.4K
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

370
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...
370
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

225
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...
225
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

341
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. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
341

You might also read

Related Articles

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

Sort by
Same author

Stroke on ECG: a cerebral T-wave change secondary to acute carbon monoxide poisoning.

Undersea & hyperbaric medicine : journal of the Undersea and Hyperbaric Medical Society, Inc·2024
Same author

The transcription factor OsNAC5 regulates cadmium accumulation in rice.

Ecotoxicology and environmental safety·2024
Same author

3D-printed silicate porous bioceramics promoted the polarization of M2-macrophages that enhanced the angiogenesis in bone regeneration.

Journal of biomedical materials research. Part B, Applied biomaterials·2024
Same author

Knockdown of HM13 Inhibits Metastasis, Proliferation, and M2 Macrophage Polarization of Non-small Cell Lung Cancer Cells by Suppressing the JAK2/STAT3 Signaling Pathway.

Applied biochemistry and biotechnology·2024
Same author

Unraveling the impact of irritability on esophageal diseases: Insights from multivariable Mendelian randomization analysis.

Journal of affective disorders·2024
Same author

High-sensitive sensory neurons exacerbate rosacea-like dermatitis in mice by activating γδ T cells directly.

Nature communications·2024

Related Experiment Video

Updated: Jul 12, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.7K

Multi-Object Pedestrian Tracking Using Improved YOLOv8 and OC-SORT.

Xin Xiao1, Xinlong Feng1

  • 1College of Mathematics and Systems Science, Xinjiang University, Urumqi 830046, China.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances pedestrian tracking for autonomous driving by improving the YOLOv8 object detection model with advanced techniques. The optimized model achieves higher accuracy and efficiency, significantly boosting performance on benchmark datasets.

Keywords:
GhostNetOC-SORTYOLOv8multi-object pedestrian trackingobject detection

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
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

9.0K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K

Related Experiment Videos

Last Updated: Jul 12, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
12:39

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

7.7K
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

9.0K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Multi-object pedestrian tracking is vital for autonomous driving systems.
  • Accurate environmental perception relies on robust pedestrian tracking.

Purpose of the Study:

  • To develop a comprehensive pedestrian tracking approach for autonomous driving.
  • To improve the efficiency and accuracy of pedestrian detection and tracking algorithms.

Main Methods:

  • An improved YOLOv8 object detection model was trained on the Crowdhuman dataset.
  • Advanced techniques including softNMS, GhostConv, and C3Ghost Modules were integrated.
  • The enhanced YOLOv8 model was combined with the OC-SORT tracking algorithm for pedestrian tracking.

Main Results:

  • Precision increased by 3.38% and mAP@0.5:0.95 by 3.07% with a 39.98% parameter reduction.
  • Model size was reduced by 37.1%, leading to more efficient detection.
  • On MOT17, HOTA reached 49.92% and MOTA reached 56.55%; on MOT20, HOTA reached 48.326% and MOTA reached 61.077%.

Conclusions:

  • The proposed approach significantly enhances pedestrian detection accuracy and efficiency.
  • The integrated YOLOv8 and OC-SORT system demonstrates superior performance in complex tracking scenarios.
  • This work contributes to more reliable autonomous driving perception systems.