A Study on Nonlinear Classifier-Based Moving Object Tracking
In the present article, we have provided a performance analysis of different classifier-based object tracking techniques. Here, object tracking has been considered as a binary classification problem. Different classifiers used in the present work are k-nearest neighbor (k-NN), fuzzy k-nearest neighbor (fuzzy k-NN), multilayer perceptron (MLP) neural network, and radial basis function (RBF) neural network. The object scale changes are controlled by considering an adaptive tracking technique. The present work is tested with different video sequences. However, for page constraints, we have provided results on two benchmark video sequences only. The performance of different object tracking techniques were evaluated by two evaluation measures: overlapped area and centroid distance.
KeywordsObject tracking k-nearest neighbor Fuzzy k-nearest neighbor Multilayer perceptron Radial basis function
Authors would like to thank Mr. Tuhin Dutta for his help during the coding.
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