Yazar "Karabacak, Cesur" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Importance Ranking of Features for Human Micro-Doppler Classification with a Radar Network
    (Ieee, 2013) Gurbuz, Sevgi Zubeyde; Tekeli, Burkan; Yuksel, Melda; Karabacak, Cesur; Gurbuz, Ali Cafer; Guldogan, Mehmet Burak
    Over the past decade, the human micro-Doppler signature has been a subject of intense research. In particular, much work has been done in relation to computing features for use in a variety of classification problems, such as arm swing detection, activity classification, and target identification. Although dozens of features have been proposed for these purposes, little work has examined the issue of which features are more important - i.e., have a greater impact on classification performance - than others. In this work, an information theoretic approach is applied to compute the importance ranking of features prior to classification for the specific problem of discriminating human walking from running. Results show that the ranking of features according to mutual information directly relates to classification performance using support vector machines.
  • Küçük Resim Yok
    Öğe
    Knowledge Exploitation for Human Micro-Doppler Classification
    (Ieee-Inst Electrical Electronics Engineers Inc, 2015) Karabacak, Cesur; Gurbuz, Sevgi Z.; Gurbuz, Ali C.; Guldogan, Mehmet B.; Hendeby, Gustaf; Gustafsson, Fredrik
    Micro-Doppler radar signatures have great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data need to be complemented with accurate simulated radar data to be able to successfully design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment, any desired scenario may be generated. Motion capture (MOCAP) has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of MOCAP data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, particularly in the case when the total amount of data is small.