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

Modeling and Similitude01:12

Modeling and Similitude

859
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
859
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

1.0K
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...
1.0K
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

751
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...
751
Orthogonal Trajectories01:26

Orthogonal Trajectories

303
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...
303
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

839
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...
839

You might also read

Related Articles

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

Sort by
Same author

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

DiMuS: Disentangled Multi-Signal Learning for Weakly Supervised Point-Based 3D Object Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Visual-Textual Information-Driven Tactile Data Generation Method.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Class Sensitive Calibration and Discrepancy-Aware Synthesis for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics·2026
Same author

Diffusion-based cross-staining feature transformation for whole slide image analysis: From H&E to IHC representation learning.

Medical image analysis·2026
Same author

High-Quality Entity Segmentation and Grounding.

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

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: May 1, 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

548

Robust object tracking via sparse collaborative appearance model.

Wei Zhong, Huchuan Lu, Ming-Hsuan Yang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 2, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a robust object tracking algorithm using a sparse collaborative model. It effectively handles appearance changes by combining holistic templates and local features, outperforming existing methods.

    More Related Videos

    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

    7.0K

    Related Experiment Videos

    Last Updated: May 1, 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

    548
    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

    7.0K

    Area of Science:

    • Computer Vision
    • Machine Learning

    Background:

    • Object tracking is crucial for video analysis.
    • Drastic appearance changes pose significant challenges for existing trackers.
    • Robustness against variations in illumination, scale, and pose is essential.

    Purpose of the Study:

    • To develop a robust object tracking algorithm.
    • To address the challenge of drastic appearance changes in object tracking.
    • To improve tracking accuracy and reduce drift.

    Main Methods:

    • A sparse collaborative model integrating holistic templates and local representations.
    • A sparse discriminative classifier (SDC) for foreground-background separation.
    • A sparse generative model (SGM) using a histogram-based approach with spatial patch information.

    Main Results:

    • The proposed algorithm demonstrates robustness against significant appearance variations.
    • Effective handling of tracking drift through an adaptive update scheme.
    • Favorable performance compared to several state-of-the-art object tracking algorithms in challenging video sequences.

    Conclusions:

    • The sparse collaborative model offers a powerful framework for robust object tracking.
    • The integration of SDC and SGM effectively tackles appearance changes.
    • The proposed tracker provides a reliable solution for complex real-world tracking scenarios.