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
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