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Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data
Citation key 1515Kutschbach2017
Author Tino Kutschbach and Erik Bochinski and Volker Eiselein and Thomas Sikora
Title of Book International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017
Pages 1–5
Year 2017
Address Lecce, Italy
Month aug
Note ISBN:978-1-5386-2939-0/17
Abstract This 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.
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