Improved Object Tracking Using Radial BasisFunction Neural Networks

سال انتشار: 1390
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 1,326

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شناسه ملی سند علمی:

ICMVIP07_104

تاریخ نمایه سازی: 28 مرداد 1391

چکیده مقاله:

In the present paper, an improved method for objecttracking is proposed using Radial Basis Function NeuralNetworks. Here, the Pixel-based color features of object are usedto develop an extended background model. The object andextended background color features are then used to train RBFNeural Network. The trained RBFNN will detect and track objectin subsequent frames. The performance of the proposed trackeris tested with many video sequences. The proposed tracker isillustrated to be suitable for real-time object tracking due to itslow computational complexity

کلیدواژه ها:

component ، object tracking ، k-means segmentation ، radial basis function neural networks

نویسندگان

Alireza Asvadi

Department of ECE, DSP Lab.Babol University of TechnologyBabol, Iran

MohammadReza Karami-Mollaie

Department of ECE, DSP Lab.Babol University of TechnologyBabol, Iran

Yasser Baleghi

Department of ECE, DSP Lab.Babol University of TechnologyBabol, Iran

Hosein Seyyedi-Andi

Department of ECE, DSP Lab.Babol University of TechnologyBabol, Iran