A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor

Abstract

Pedestrian detection is a fundamental problem in various computer vision applications and is addressed by complex solutions involving complex feature extractor and classification techniques. In this paper, feature selector is used along with Histogram of Significant Gradients (HSG) descriptor and linear SVM classifier to enhance the detection accuracy and reduce the processing time. A feature selector organizes the extracted features in decreasing order of their significance. A comparative study has been done employing different classifiers and feature selectors and a system is proposed with better detection capability. The proposed scheme is found to have a 50 % reduction in feature size, 67 % reduction in processing time and 3.93 % improvement in accuracy.

Application of an Estimation Method in the Lure System

“…e new system is known as the state observer. Many state observers were designed to eliminate the random disturbances [1][2][3][4][5], including several robust designs under bounded uncertainty [6][7][8][9][10][11][12].…”

Classification of Microarray Data Using Kernel Based Classifiers

“…Zhang et al [8] have studied the brain cancer and proposes various feature selection and kernel based classification models. They have improved the prediction accuracy of GBM prognosis mRMR and Multiple Kernel Machine (MKL) learning method [20]. The main objective was to propose an ensemble method which predicts GBM prognosis with high accuracy.…”