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 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...
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...
Inertial Frames of Reference01:03

Inertial Frames of Reference

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 constant...
Non-inertial Frames of Reference01:27

Non-inertial Frames of Reference

A reference frame accelerating or decelerating relative to an inertial frame is a non-inertial frame. To help understand this, consider what taking off in an airplane, turning a corner in a car, riding a merry-go-round, and the circular motion of a tropical cyclone all have in common. All these systems are accelerating, decelerating, or rotating relative to the Earth; hence, they all are non-inertial frames. All these systems exhibit inertial forces, which merely seem to arise from motion,...
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...
Orthogonal Trajectories01:26

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...

You might also read

Related Articles

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

Sort by
Same author

How Trustworthy are Performance Evaluations for Basic Vision Tasks?

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Biological data annotation via a human-augmenting AI-based labeling system.

NPJ digital medicine·2021
Same author

Embodied intelligence via learning and evolution.

Nature communications·2021
Same author

JRDB: A Dataset and Benchmark of Egocentric Robot Visual Perception of Humans in Built Environments.

IEEE transactions on pattern analysis and machine intelligence·2021
Same author

Lactation improves pancreatic β cell mass and function through serotonin production.

Science translational medicine·2020
Same author

Watch-n-Patch: Unsupervised Learning of Actions and Relations.

IEEE transactions on pattern analysis and machine intelligence·2017
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

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

Related Experiment Video

Updated: May 11, 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

A general framework for tracking multiple people from a moving camera.

Wongun Choi1, Caroline Pantofaru, Silvio Savarese

  • 1Department of Electrical and Computer Engineering, University of Michigan, Room 4435, 1301 Beal Avenue, Ann Arbor, MI 48109-2122, USA. wgchoi@umich.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for tracking multiple people using mobile vision, simultaneously estimating camera motion and pedestrian paths for robust 3D trajectory determination.

More Related Videos

Individual Culturing of Tigriopus Copepods and Quantitative Analysis of Their Mate-guarding Behavior
06:24

Individual Culturing of Tigriopus Copepods and Quantitative Analysis of Their Mate-guarding Behavior

Published on: September 26, 2018

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

Related Experiment Videos

Last Updated: May 11, 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

Individual Culturing of Tigriopus Copepods and Quantitative Analysis of Their Mate-guarding Behavior
06:24

Individual Culturing of Tigriopus Copepods and Quantitative Analysis of Their Mate-guarding Behavior

Published on: September 26, 2018

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

Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Accurate tracking of multiple individuals from mobile platforms is crucial for applications like autonomous driving and surveillance.
  • Estimating camera ego-motion and human trajectories simultaneously presents a significant challenge in dynamic environments.

Purpose of the Study:

  • To develop a unified framework for robustly tracking multiple people and estimating camera ego-motion from a mobile vision platform.
  • To achieve accurate 3D trajectory estimation for both the camera and individuals, even in complex, interactive scenarios.

Main Methods:

  • A general framework integrating camera ego-motion estimation and multi-person tracking within a single probabilistic model.
  • Utilizing the reversible jump Markov chain Monte Carlo (RJ-MCMC) particle filtering method to find the Maximum A Posteriori (MAP) solution.

Main Results:

  • The proposed method demonstrates robust estimation of camera motion in dynamic scenes.
  • Stable and accurate tracking of independently moving and interacting individuals was achieved.
  • Successful evaluation on challenging outdoor street and indoor RGB-D datasets.

Conclusions:

  • The developed framework provides a robust solution for simultaneous camera ego-motion and multi-person tracking.
  • The RJ-MCMC particle filtering approach effectively handles complex dynamic environments and interactions.
  • This work advances the capabilities of mobile vision systems for human behavior analysis.