학술논문
Sequential sensor fusion combining probability hypothesis density and kernelized correlation filters for multi-object tracking in video data
Document Type
Conference
Author
Source
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on. :1-5 Aug, 2017
Subject
Language
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.