Application of optimized machine learning techniques for prediction of occupational accidents

Object detection and tracking is a vital task among computer vision researchers. The main objective of object detection and tracking is to establish correspondence of objects and object parts between consecutive frames of video. It became difficult since the object faces some problems usually shape deformation, occlusion, scale change, drift etc. There are various algorithm used for tracking. In this project, tracking is performed by using HOG and SVM classifier. The Histogram of Oriented Gradients (HOG) is a feature/image descriptor used in image Processing and other visual areas for the purpose of object detection. The HOG is capable (Histogram of Oriented Gradients) to distinguish the target and the background with HOG visualization and the technique counts occurrences of gradient orientation in localized portions of an image. Support Vector Machine (SVM) is the classifier used for classification. SVM with a single kernel is used in this project. The radial basis function kernel (RBF kernel) SVM are used for training and tracking. The HOG and SVM combination makes our system more efficient. And also we use RGB histogram and SIFT to describe the image.

Object Tracking using HOG and SVM

Recent studies have showed that machine learning techniques are advantageous to statistical models for medicine database classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. Three UCI databases are used to demonstrate the good performance of the SVM- MK.

Data Classification Using Support Vector Machines with Mixture Kernels

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Data Classification Using Support Vector Machines with Mixture Kernels