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Relative Motion Analysis using Rotating Axes

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...

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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

Dynamic Collaborative Tracking.

Guibo Zhu, Zhaoxiang Zhang, Jinqiao Wang

IEEE Transactions on Neural Networks and Learning Systems

|March 17, 2020

Summary

This summary is machine-generated.

This study introduces a dynamic collaborative tracking framework to combat model drift in visual tracking. The unified approach enhances object tracking robustness against occlusion and motion challenges.

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Area of Science:

Computer Vision
Machine Learning

Background:

Correlation filters achieve success in visual tracking.
Existing methods suffer from model drift due to occlusion, motion, and distractors.

Purpose of the Study:

To propose a unified dynamic collaborative tracking framework for robust object position prediction.
To address model drift issues in visual tracking.

Main Methods:

A framework jointly trains target regression, distracter suppression, and maximum margin relation submodules.
Utilizes circulant structure for target-background distinction.
Optimizes distracter regions and employs discriminative mapping space for hard negative samples.
Integrates a CUR filter as an assistant detector.

Main Results:

The proposed framework demonstrates enhanced flexibility and robustness in position prediction.
Achieves state-of-the-art performance on benchmark datasets.
Effectively alleviates model drift caused by various factors.

Conclusions:

The unified dynamic collaborative tracking framework offers a significant improvement over existing methods.
The approach provides a robust solution for challenging visual tracking scenarios.