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

Orthogonal Trajectories

Orthogonal Trajectories

Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...

Design Example: Measuring Distance Between Two Points with Obstructions

Design Example: Measuring Distance Between Two Points with Obstructions

When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...

Collisions in Multiple Dimensions: Problem Solving

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 Solving

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...

Vector Algebra: Graphical Method

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...

Elastic Collisions: Case Study

Elastic Collisions: Case Study

Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...

You might also read

Related Articles

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

Sort by

Same author

How to estimate carbon footprint when training deep learning models? A guide and review.

Environmental research communications·2023

Same author

GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays.

Pattern recognition·2021

Same author

Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021

Same author

Variational Osmosis for Non-linear Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020

Same author

SuperPatchMatch: An Algorithm for Robust Correspondences Using Superpixel Patches.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2017

Same author

An Optimized PatchMatch for multi-scale and multi-feature label fusion.

NeuroImage·2015

Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026

Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligence·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

Tracking with occlusions via graph cuts.

Nicolas Papadakis¹, Aurélie Bugeau

¹Image Group, Barcelona Media, Barcelona, Spain. nicolas.papadakis@barcelonamedia.org

IEEE Transactions on Pattern Analysis and Machine Intelligence

|November 20, 2010

Summary

This summary is machine-generated.

This study introduces a novel method for tracking and segmenting interacting objects in image sequences by formalizing visible and occluded parts. The approach effectively handles occlusions using dynamical laws and graph cuts optimization for robust object tracking.

More Related Videos

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Related Experiment Videos

Last Updated: Jun 6, 2026

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Tracking Rats in Operant Conditioning Chambers Using a Versatile Homemade Video Camera and DeepLabCut

Published on: June 15, 2020

Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Object tracking and segmentation in image sequences are challenging due to occlusions.
Existing methods often struggle with partial and complete occlusions, impacting performance.
A formal definition of visible and occluded object parts is needed for improved tracking.

Purpose of the Study:

To present a new method for tracking and segmenting time-interacting objects in image sequences.
To formalize the concepts of visible and occluded object parts for robust tracking.
To handle partial and complete occlusions effectively using predictive modeling.

Main Methods:

Objects are tracked and segmented by distinguishing between visible and occluded parts.
Object velocity is modeled using dynamical laws to predict future positions.
A graph cuts optimization framework is employed to minimize an energy function for multilabel segmentation.

Main Results:

The proposed method successfully tracks and segments objects in challenging image sequences.
The formalization of visible and occluded parts aids in managing occlusion scenarios.
The integration of prediction and segmentation demonstrates robustness in handling disappearing objects.

Conclusions:

The developed method offers a robust solution for tracking and segmenting interacting objects, particularly in the presence of occlusions.
The novel approach of separating visible and occluded parts improves tracking accuracy and segmentation quality.
This work advances the state-of-the-art in dynamic object tracking within image sequences.