| Conference/Proceedings | International Workshop on Traffic and Street Surveillance for Safety and Security at IEEE AVSS 2017 |
| Start date | 29.08.2017 |
| End date | 01.09.2017 |
| Address | Lecce, Italy |
| Pages | 1-5 |
| Author(s) | Tino Kutschbach, Erik Bochinski, Volker Eiselein, Thomas Sikora |
| Title | Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data |
| 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. |
| Key words | multimedia analysis, Multi-Object Tracking, PHD Filter, Kernelized Correlation Filter |
| Note | ISBN:978-1-5386-2939-0/17 |
| File | 1515Kutschbach2017.pdf |