Adaptive visual tracking by decision level fusion of features in a particle filtering frame work
عنوان مقاله: Adaptive visual tracking by decision level fusion of features in a particle filtering frame work
شناسه ملی مقاله: ICMVIP05_135
منتشر شده در پنجمین کنفرانس ماشین بینایی و پردازش تصویر در سال 1387
شناسه ملی مقاله: ICMVIP05_135
منتشر شده در پنجمین کنفرانس ماشین بینایی و پردازش تصویر در سال 1387
مشخصات نویسندگان مقاله:
M Komeili - Dept of Electrical Engineering, Tarbiat Modarres University
N Armanfard - Dept of Electrical Engineering, Tarbiat Modarres University
E Kabir - Dept of Electrical Engineering, Tarbiat Modarres University
خلاصه مقاله:
M Komeili - Dept of Electrical Engineering, Tarbiat Modarres University
N Armanfard - Dept of Electrical Engineering, Tarbiat Modarres University
E Kabir - Dept of Electrical Engineering, Tarbiat Modarres University
In this paper we propose a new method for multi-feature object tracking in a particle filter framework. Each particle indicates one hypothesis of tracked object. In common method of feature combination, each particle measures all features. Due to limited computational power, particle filter is forced to run with lower number of particles which results in weak approximation of posterior distribution of target state. In our method, each hypostasis is evaluated by
only one feature, so the number of particles can be increased. In our method the percentage of the particles which participate in the measurement of a specific feature is directly related to the reliability of that feature. Measuring more reliable features and having better spatially distributed samples from the scene are two main advantages of our method.
Experimental results over a set of real-world sequences demonstrate better performance of our method compared to
common method of feature combination.
کلمات کلیدی: feature combination, feature reliability, object tracking, particle filter, video sequences
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/52111/