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Related Experiment Video

Updated: May 10, 2026

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

Nonlinear dynamic model for visual object tracking on Grassmann manifolds with partial occlusion handling.

Zulfiqar Hasan Khan, Irene Yu-Hua Gu

IEEE Transactions on Cybernetics

|June 13, 2013

Summary

This study introduces a new Bayesian online tracking method for video objects using Grassmann manifolds. It effectively handles challenging nonplanar pose changes and long-term occlusions in object tracking.

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VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

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Published on: October 12, 2011

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

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Published on: December 25, 2017

Area of Science:

Computer Vision
Machine Learning
Manifold Geometry

Background:

Manifold visual object tracking shows promise but struggles with significant out-of-plane pose changes and long-term occlusions.
These challenges limit the performance of existing tracking algorithms for deformable objects in videos.

Purpose of the Study:

To propose a novel Bayesian online learning and tracking scheme for video objects on Grassmann manifolds.
To address limitations in handling nonplanar pose changes and long-term partial occlusions.

Main Methods:

Online estimation of object appearances on Grassmann manifolds.
Optimal criterion-based occlusion handling for updating object appearances.
A nonlinear dynamic model for appearance basis matrix and velocity.
Separate Bayesian formulations for tracking and online learning using two particle filters (manifold and linear space).
Alternating tracking and online updating to mitigate drift.

Main Results:

The proposed tracker demonstrates robust performance on videos with significant nonplanar pose changes and long-term partial occlusions.
Experimental comparisons validate the effectiveness against eight state-of-the-art manifold and nonmanifold trackers.

Conclusions:

The developed Bayesian online tracking scheme on Grassmann manifolds offers a robust solution for challenging visual tracking scenarios.
The method effectively handles complex object dynamics and occlusions, outperforming existing approaches.