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Collaborative Detector Fusion of Data-Driven PHD Filter for Online Multiple Human Tracking

Lookup NU author(s): Zeyu Fu, Dr Mohsen Naqvi, Professor Jonathon Chambers

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Abstract

© 2018 ISIF The use of multiple data sources (measurements) has been recently demonstrated to improve the accuracy and reliability of a tracking system as it is capable of providing redundancy in different aspects, and also eliminating interferences of individual sources. This paper focuses on addressing the multiple human tracking problem from a multi-detector approach. This approach integrates two detectors with different characteristics (full-body and body-parts) to perform robust collaborative fusion based on data-driven Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters. To leverage the maximum strengths from multiple detectors, we propose a robust fusion center at the track level, which manages to perform Generalized Intersection Covariance (GCI) fusions for survival and birth tracks independently, and also eliminates false tracks caused by a cluttered environment. Moreover, an identity reassignment mechanism is also developed to address the identity mismatching problem in the target birth process, so as to enhance the fusion performance and track consistency. Experimental results on two challenging benchmark video sequences confirm the effectiveness of the proposed approach.


Publication metadata

Author(s): Fu Z, Naqvi SM, Chambers JA

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2018 21st International Conference on Information Fusion, FUSION 2018

Year of Conference: 2018

Pages: 1976-1981

Online publication date: 06/09/2018

Acceptance date: 10/07/2018

Publisher: IEEE

URL: https://doi.org/10.23919/ICIF.2018.8455432

DOI: 10.23919/ICIF.2018.8455432

Library holdings: Search Newcastle University Library for this item

ISBN: 9780996452762


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