Detection of Moving Object in Dynamic Visual Sequences Based on Partial Least Squares Classifier

Detection of moving object from a visual sequence plays a vital role for the tracking of object. The main objective of this proposed work is to detect and classify the various video sequences with the help of different classification algorithms. The input video sequences from the publicly available datasets are collected and the individual frames are extracted. These frames are pre-processed and then applied to the novel background subtraction process. Important features based on the Local Binary Pattern (LBP) and grey level co-efficient are extracted. Finally these features are classified by three different classifiers like SVM, PLS, and PNN. The performance of these different classifiers are evaluated and compared. It is found that PLS classifier produces more classification accuracy but with more computation time.