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

Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
Body Planes01:06

Body Planes

Body planes in anatomy are imaginary flat surfaces used as reference points to divide the body into sections for anatomical study. These planes are essential for understanding the orientation, relationships, and spatial organization of anatomical structures.
The sagittal plane is the plane that divides the body or an organ vertically into right and left sides. If this vertical plane runs directly down the middle of the body resulting in equal division, it is called the midsagittal or median...
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...
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...
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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

Relative Motion Analysis using Rotating Axes

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 instrumental in...

You might also read

Related Articles

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

Sort by
Same author

STROKEVISION-BENCH: A MULTIMODAL VIDEO AND 2D POSE BENCHMARK FOR TRACKING STROKE RECOVERY.

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing·2026
Same author

Leveraging sparse annotations for leukemia diagnosis on the large leukemia dataset.

Medical image analysis·2025
Same author

Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions.

bioRxiv : the preprint server for biology·2025
Same author

DiffAct++: Diffusion Action Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Foundation Models Defining a New Era in Vision: A Survey and Outlook.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Rebuttal to "Comments on 'Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features' ".

IEEE transactions on pattern analysis and machine intelligence·2024
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
Same journal

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Achieving Text-based Person Retrieval with Any Granularity.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

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

Tracking multiple occluding people by localizing on multiple scene planes.

Saad M Khan1, Mubarak Shah

  • 1University of Central Florida, Orlando, FL 32816, USA. skhan@sarnoff.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-view approach for robustly tracking people in crowded scenes. By synergistically fusing evidence from multiple cameras, it overcomes occlusion and visibility issues without full calibration.

More Related Videos

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Related Experiment Videos

Last Updated: Jun 26, 2026

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

A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

Area of Science:

  • Computer Vision
  • Multi-view Geometry
  • Human Tracking

Background:

  • Occlusion and poor visibility in crowded scenes hinder accurate single-view person tracking.
  • Existing multi-view methods often require full camera calibration, limiting their applicability.

Purpose of the Study:

  • To develop a purely image-based, multi-view approach for reliable person detection and tracking in challenging environments.
  • To overcome limitations of single-view tracking by leveraging synergistic information fusion.

Main Methods:

  • A planar homographic occupancy constraint fuses foreground likelihoods from multiple views.
  • Utilizes plane-to-plane homologies for extended robustness across parallel planes.
  • Incorporates a clutter measure to weight views based on scene clarity.
  • Employs graph cuts for simultaneous detection and tracking in space-time occupancy data.

Main Results:

  • Successfully resolves occlusions and localizes people on a reference scene plane.
  • Demonstrates robustness through extension to multiple parallel planes.
  • Achieves accurate detection and tracking in complex, crowded, multi-view scenarios.
  • Provides detailed qualitative and quantitative performance analysis.

Conclusions:

  • The proposed image-based multi-view fusion framework effectively addresses occlusion and visibility challenges in crowded scenes.
  • This method offers a robust and practical solution for multi-view person tracking without requiring camera calibration.