TBD (track-before-detect) refers to the tracking paradigm where measurements about a tentative target are integrated over time and may yield detection in cases when signals from any particular time instance are too weak against clutter (low signal-to-noise ratio) to register a detected target. The Hough transform line detection method has been established as a viable technique for TBD. However, its basic operation of binning and accumulating votes in the parameter space is computationally expensive. A more critical weakness of Hough transform is its dependence on parameter tuning (e.g., bin size and various thresholds), which can be non-intuitive and data-dependent. This leads to low detection rates in data with low signal-to-noise ratio and significant clutter. In this seminar we present a plane sweep based TBD methods and explore its advantage over HT based methods.
Yanmei Guo graduated from BUPT (Beijing University of Post and Telecommunications), Beijing China with B.Sc. and M.Sc. degree in Electrical and Electronic Engineering in 2002 and 2005 respectively. She has been working in industry and academic sector since 2005 as a software engineer. From Oct 2010, she is a PhD student at the University of Adelaide, Australia. Her main research activities are in the field of TBD (Track Before Detect), parametric line fitting and non-parametric statistical learning.
|Thursday, 2nd July, 2015
322 Hughes Bldg