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FUSION OF MOTION SEGMENTATION WITH ONLINE ADAPTIVE NEURAL CLASSIFIER FOR ROBUST TRACKING

Topics: 2D and 3D Scene Understanding; Detecting 3D Objects Using Patterns of Motion and Appearance; Feature Extraction; Model-Based Object Tracking in Image Sequences; Motion and Tracking; Neural Networks; Retrieval of 3D Objects from Video Sequences; Tracking of People and Surveillance; Visual Learning

Authors: Sławomir Bąk 1 ; Sundaram Suresh 2 ; François Brémond 2 and Monique Thonnat 2

Affiliations: 1 Institute of Computing Science, Poznan University of Technology, Poland ; 2 INRIA Sophia Antipolis, PULSAR group, France

ISBN: 978-989-8111-69-2

ISSN: 2184-4321

Keyword(s): Object tracking, Neural network, Gaussian activation function, Feature extraction, On-line learning, Motion segmentation, Reliability classification.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Detecting 3D Objects Using Patterns of Motion and Appearance ; Feature Extraction ; Features Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Methodologies and Methods ; Model-Based Object Tracking in Image Sequences ; Motion and Tracking ; Motion, Tracking and Stereo Vision ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Retrieval of 3D Objects from Video Sequences ; Sensor Networks ; Signal Processing ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Theory and Methods ; Tracking of People and Surveillance

Abstract: This paper presents a method to fuse the information from motion segmentation with online adaptive neural classifier for robust object tracking. The motion segmentation with object classification identify new objects present in the video sequence. This information is used to initialize the online adaptive neural classifier which is learned to differentiate the object from its local background. The neural classifier can adapt to illumination variations and changes in appearance. Initialized objects are tracked in following frames using the fusion of their neural classifiers with the feedback from the motion segmentation. Fusion is used to avoid drifting problems due to similar appearance in the local background region. We demonstrate the approach in several experiments using benchmark video sequences with different level of complexity.

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Paper citation in several formats:
Bąk S.; Suresh S.; Brémond F.; Thonnat M. and (2009). FUSION OF MOTION SEGMENTATION WITH ONLINE ADAPTIVE NEURAL CLASSIFIER FOR ROBUST TRACKING.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 410-416. DOI: 10.5220/0001769604100416

@conference{visapp09,
author={Sławomir Bąk and Sundaram Suresh and Fran\c{C}ois Brémond and Monique Thonnat},
title={FUSION OF MOTION SEGMENTATION WITH ONLINE ADAPTIVE NEURAL CLASSIFIER FOR ROBUST TRACKING},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={410-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001769604100416},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2009)
TI - FUSION OF MOTION SEGMENTATION WITH ONLINE ADAPTIVE NEURAL CLASSIFIER FOR ROBUST TRACKING
SN - 978-989-8111-69-2
AU - Bąk, S.
AU - Suresh, S.
AU - Brémond, F.
AU - Thonnat, M.
PY - 2009
SP - 410
EP - 416
DO - 10.5220/0001769604100416

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