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• W2891553219 abstract "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." @default.
• W2891553219 created "2018-09-27" @default.
• W2891553219 creator A5012939991 @default.
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• W2891553219 date "2018-07-01" @default.
• W2891553219 modified "2023-09-29" @default.
• W2891553219 title "Collaborative Detector Fusion of Data-Driven PHD Filter for Online Multiple Human Tracking" @default.
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• W2891553219 doi "https://doi.org/10.23919/icif.2018.8455432" @default.
• W2891553219 hasPublicationYear "2018" @default.
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