期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 59, 期 -, 页码 257-271出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2019.01.026
关键词
Visual tracking; Random finite sets; FISST; Multiple target filtering; PHD filter; N-type GM-PHD filter; Gaussian mixture; OSPA metric
资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/K009931]
- James Watt Scholarship
- EPSRC [EP/K009931/1] Funding Source: UKRI
We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple targets having N >= 2 different types based on Random Finite Set theory, taking into account not only background clutter, but also confusions among detections of different target types, which are in general different in character from background clutter. Under Gaussianity and linearity assumptions, our framework extends the existing Gaussian mixture (GM) implementation of the standard PHD filter to create a N-type GM-PHD filter. The methodology is applied to real video sequences by integrating object detectors' information into this filter for two scenarios. For both cases, Munkres's variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. This approach is evaluated and compared to both raw detection and independent GM-PHD filters using the Optimal Sub-pattern Assignment metric and discrimination rate. This shows the improved performance of our strategy on real video sequences. (C) 2019 Elsevier Inc. All rights reserved.
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