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Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

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Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking.

Filiz Bunyak¹, Kannappan Palaniappan, Sumit Kumar Nath

¹Department of Computer Science, University of Missouri-Columbia, MO 65211-2060, USA, Email: {bunyak,palaniappank}@missouri.edu , naths@ecse.rpi.edu.

Journal of Multimedia

|December 20, 2008

Summary

This study introduces a novel flux tensor method for accurate infrared motion detection, enhancing object tracking systems. The approach effectively handles shadows and illumination changes in multispectral videos.

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

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

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

Published on: January 18, 2020

Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Object tracking systems are crucial for surveillance and analysis.
Existing methods struggle with complex scenes, varying illumination, and shadows.
Multispectral data fusion offers potential for robust tracking.

Purpose of the Study:

To develop an advanced object tracking system using novel motion detection and segmentation algorithms.
To improve accuracy and robustness in challenging environmental conditions.
To enable reliable tracking in multispectral video data.

Main Methods:

Utilized a flux tensor algorithm for efficient motion detection in infrared video, independent of background modeling.
Employed level set-based geodesic active contour evolution for object segmentation, fusing visible color and infrared edge information.
Integrated a shape-based model for refining touching or overlapping objects.
Extended multiple object tracking using correspondence graphs, Kalman filter-based cluster trajectory analysis, and watershed segmentation to handle occlusions and object groups.

Main Results:

The flux tensor motion detector demonstrated higher accuracy than traditional methods in infrared video.
The system proved insensitive to shadows and illumination variations across visible and infrared channels.
The object tracking algorithm successfully handled challenging outdoor multispectral videos with occlusions and complex scenes.

Conclusions:

The proposed flux tensor-based motion detection and multispectral fusion approach significantly enhances object tracking performance.
The system offers a robust solution for real-world monitoring tasks under adverse conditions.
This research contributes a more accurate and reliable object tracking system for diverse applications.