文章基本信息

标题：Optimized UAV object tracking framework based on Integrated Particle filter with ego-motion transformation matrix
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作者：Wesam Askar ; Wesam Askar ; Osama Elmowafy 等
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2017
卷号：125
页码：1-8
DOI：10.1051/matecconf/201712504027
语种：English
出版社：EDP Sciences
摘要：Vision based object tracking problem still a hot and important area of research specially when the tracking algorithms are performed by the aircraft unmanned vehicle (UAV). Tracking with the UAV requires special considerations due to the flight maneuvers, environmental conditions and aircraft moving camera. The ego motion calculations can compensate the effect of the moving background resulted from the moving camera. In this paper an optimized object tracking framework is introduced to tackle this problem based on particle filter. It integrates the calculated ego motion transformation matrix with the dynamic model of the particle filter during the prediction stage. Then apply the correction stage on the particle filter observation model which based on two kinds of features includes Haar-like Rectangles and edge orientation histogram (EOH) features. The Gentle AdaBoost classifier is used to select the most informative features as a preliminary step. The experimental results achieved more than 94.6% rate of successful tracking during different scenarios of the VIVID database in real time tracking speed.