conference paper


Conference/ProceedingsInternational Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017
Start date29.08.2017
End date01.09.2017
AddressLecce, Italy
Pages1-5
Author(s)Tino Kutschbach, Erik Bochinski, Volker Eiselein, Thomas Sikora
TitleSequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data
AbstractThis work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to multi-object tracking in video data. In order to take advantage of additional visual information, Kernelized Correlation Filters(KCF) are evaluated as a possible extension of the GMPHD tracking-by-detection scheme to enhance its performance. The baseline GMPHD filter and its extension are evaluated on the UA-DETRAC benchmark, showing that combining both methods leads to a higher recall and a better quality of object tracks to the cost of increased computational complexity and increased sensitivity to false-positives.
Key wordsMulti-Object Tracking, PHD Filter, Kernelized Correlation Filter
NoteISBN:978-1-5386-2939-0/17
File1515Kutschbach2017.pdf

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