کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6866871 | 679063 | 2014 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Ego motion guided particle filter for vehicle tracking in airborne videos
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
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چکیده انگلیسی
Tracking in airborne circumstances is receiving more and more attention from researchers, and it has become one of the most important components in video surveillance for its advantage of better mobility, larger surveillance scope and so on. However, airborne vehicle tracking is very challenging due to the factors such as platform motion, scene complexity, etc. In this paper, to address these problems, a new framework based on Kanade-Lucas-Tomasi (KLT) features and particle filter is proposed. KLT features are tracked throughout the video sequence. At the beginning of video tracking, a strategy based on motion consistence with RANSAC is utilized to separate background KLT features. The grouping of background features helps estimate the ego motion of the platform and the estimation is then incorporated into the prediction step in particle filter. Color similarity and Hu moments are used in the measurement model to assign the weights of particles. Our experimental results demonstrated that the proposed method outperformed the other tracking methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 124, 26 January 2014, Pages 168-177
Journal: Neurocomputing - Volume 124, 26 January 2014, Pages 168-177
نویسندگان
Xianbin Cao, Changcheng Gao, Jinhe Lan, Yuan Yuan, Pingkun Yan,