@INPROCEEDINGS{1515Kutschbach2017,
	AUTHOR = {Tino Kutschbach and Erik Bochinski and Volker Eiselein and Thomas Sikora},
	TITLE = {Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data},
	BOOKTITLE = {International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017},
	YEAR = {2017},
	MONTH = aug,
	PAGES = {1--5},
	ADDRESS = {Lecce, Italy},
	NOTE = {ISBN:978-1-5386-2939-0/17},
	PDF = {http://elvera.nue.tu-berlin.de/files/1515Kutschbach2017.pdf},
	URL = {http://elvera.nue.tu-berlin.de/files/1515Kutschbach2017.pdf},
	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.}
}