|International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017
|Tino Kutschbach, Erik Bochinski, Volker Eiselein, Thomas Sikora
|Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data
|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.
|multimedia analysis, Multi-Object Tracking, PHD Filter, Kernelized Correlation Filter