Improved Object Tracking Using Radial Basis Function Neural Networks
Alireza Asvadi, MohammadReza Karami-Mollaie, Yasser Baleghi, Hosein Seyyedi-Andi · 2011
In the present paper, an improved method for object tracking is proposed using Radial Basis Function Neural Networks. Here, the Pixel-based color features of object are used to develop an extended background model. The object and extended background color features are then used to train RBF Neural Network. The trained RBFNN will detect and track object in subsequent frames. The performance of the proposed tracker is tested with many video sequences. The proposed tracker is illustrated to be suitable for real-time object tracking due to its low computational complexity.