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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.7K
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.
1.7K
Perceptual Constancy01:12

Perceptual Constancy

1.1K
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
1.1K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.3K
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
1.3K
Three-Dimensional Force System01:30

Three-Dimensional Force System

2.7K
In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
2.7K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

648
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...
648
Inertial Frames of Reference01:03

Inertial Frames of Reference

8.5K
Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
8.5K

You might also read

Related Articles

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

Sort by
Same author

Automatic Data Reduction of Image Sequences Acquired in Object Tracking Mode for Detection and Position Measurement of Faint Orbital Objects.

Sensors (Basel, Switzerland)·2026
Same author

Fully Convolutional Neural Network for Vehicle Speed and Emergency-Brake Prediction.

Sensors (Basel, Switzerland)·2024
Same author

Part-Based Obstacle Detection Using a Multiple Output Neural Network.

Sensors (Basel, Switzerland)·2022
Same author

Robust Data Association Using Fusion of Data-Driven and Engineered Features for Real-Time Pedestrian Tracking in Thermal Images.

Sensors (Basel, Switzerland)·2021
Same author

High-Speed Video System for Micro-Expression Detection and Recognition.

Sensors (Basel, Switzerland)·2017

Related Experiment Video

Updated: Dec 27, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.5K

A Self-Calibrating Probabilistic Framework for 3D Environment Perception Using Monocular Vision.

Razvan Itu1, Radu Danescu1

  • 1Technical University of Cluj-Napoca, St. Memorandumului 28, 400114 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|March 4, 2020
PubMed
Summary

This study introduces a new framework for vehicle obstacle detection using a single camera. It leverages Convolutional Neural Network (CNN) segmentation and a dynamic occupancy grid to extract 3D data, enhancing traffic safety.

Keywords:
camera calibrationmeasurement modelmonocular visionobstacle detection

More Related Videos

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.4K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.3K

Related Experiment Videos

Last Updated: Dec 27, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.5K
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

2.4K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.3K

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Stereovision systems offer direct 3D data but require multiple cameras.
  • Single cameras, like dashcams or phone cameras, are widely accessible and easily integrated into vehicles.
  • Extracting reliable 3D obstacle information from monocular vision remains a challenge.

Purpose of the Study:

  • To present a framework for extracting and tracking 3D obstacle data using a single, generic camera in a vehicle.
  • To develop a system that overcomes the limitations of monocular vision for 3D perception.
  • To enhance vehicle environment perception and traffic safety.

Main Methods:

  • Utilizing Convolutional Neural Network (CNN)-based segmentation to process monocular images.
  • Employing a dynamic occupancy grid as a probabilistic model for environmental representation.
  • Developing a method for automatic calibration of intrinsic and extrinsic camera parameters without user intervention.
  • Integrating segmentation, probabilistic modeling, and calibration into a scene tracking system.

Main Results:

  • A probabilistic measurement model generated from monocular images, accounting for monocular vision's limitations.
  • Fully automatic calibration of camera parameters, eliminating the need for manual setup.
  • A robust scene tracking system capable of perceiving obstacles in real traffic conditions using any camera.
  • Demonstrated enhancement of environment perception for vehicles.

Conclusions:

  • The proposed framework effectively extracts and tracks 3D obstacle data from monocular camera input.
  • Automatic calibration and CNN-based segmentation significantly improve the feasibility of single-camera 3D perception.
  • The system offers a versatile solution for enhancing vehicle safety, adaptable to various vehicles and sensor configurations.