论文成果
Particle Filter Tracking in Low Frame Rate Video
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关键字:Particle filter; Detection; Object Tracking; Low Frame Rate
摘要:Object tracking algorithm using modified Particle filter in low frame rate (LFR) video is proposed in this paper, which the object moving significantly and randomly between consecutive frames in the low frame rate situation. Traditionally, Particle filtering use motion transitions to model the movement of the target. However, in object tracking with low frame rate sequences, it is very difficult to model significant random jumps of subjects. The key notion of our solution is that using the object detection and extraction to locate the tracked object, while not using the dynamical function. We propagate the sample set around the detected regions, which the samples are assumed to be uniformly distributed in the neighborhoods of the detected region. It is similar to the general particle filter to propagate samples. Then we compute the likelihood between the target model and the candidate regions, which are based on color histogram distances. Our extensive experiments show that the proposed algorithm performs robustly in a large variety of tracking scenarios..
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张涛

教师拼音名称:zhangtao

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所在单位:自动化教研室

学历:博士研究生

学位:博士

毕业院校:东南大学

所属院系:自动化与电子工程学院

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