Jove
Visualize
Contact Us

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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...
Fluid Movement Between Compartments01:18

Fluid Movement Between Compartments

The force applied by fluids against a surface, known as hydrostatic pressure, initiates the transfer of fluid among different compartments. Within our blood vessels, the blood's hydrostatic pressure is a result of the heart's pumping action. At the arteriolar end of capillaries, hydrostatic pressure (capillary blood pressure) exceeds the opposing colloid osmotic pressure created primarily by plasma proteins like albumin. This discrepancy in pressure propels plasma and nutrients from the...

You might also read

Related Articles

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

Sort by
Same author

A Cox Rate-and-State Model for Monitoring Seismic Hazard in the Groningen Gas Field.

Mathematical geosciences·2026
See all related articles
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 Experiment Video

Updated: Jul 4, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

Depth map calculation for a variable number of moving objects using markov sequential object processes.

M N M van Lieshout1

  • 1Centrum voor iskunde en Informatica, Kruislaan, Amsterdam, The Netherlands. colette@cwi.nl

IEEE Transactions on Pattern Analysis and Machine Intelligence
|June 14, 2008
PubMed
Summary
This summary is machine-generated.

We introduce Markov sequential object processes for tracking multiple moving objects in videos to calculate depth. This method optimizes object tracks and their associated depths for improved video analysis.

More Related Videos

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

Related Experiment Videos

Last Updated: Jul 4, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow
13:02

Three-dimensional Particle Tracking Velocimetry for Turbulence Applications: Case of a Jet Flow

Published on: February 27, 2016

Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Tracking a variable number of objects in video is challenging.
  • Depth calculation from video requires robust object tracking.

Purpose of the Study:

  • To propose Markov sequential object processes for multi-object tracking and depth estimation.
  • To develop a regression model for quantifying fit and regularization for object interactions.

Main Methods:

  • Utilizing Markov sequential object processes for tracking.
  • Employing a regression model with regularization terms.
  • Implementing a Markov chain Monte Carlo method for optimization.

Main Results:

  • Demonstrated effectiveness on synthetic and real-world sports video data.
  • Successfully identified optimal object tracks and associated depths.
  • Quantified goodness of fit for tracking and depth calculation.

Conclusions:

  • Markov sequential object processes offer a viable approach for multi-object tracking and depth estimation.
  • The developed method provides a robust framework for analyzing dynamic scenes.
  • This technique has potential applications in various fields requiring 3D scene understanding from video.