Analysis of Combination Knowledge Acquisition of Haar Training for Object Detection on the Viola Jones Method

Abstract

Viola Jones method uses the file classifier to object detection. The training process to create object classifier file requires very high computer resources and time which is directly proportional to the amount of training data. The amount of training data determines the accuracy of object detection. The long training process is caused because the computer has low specifications and the distribution of Haartraining files will speed up the process of vector file formation, minimize errors when cutting Haar features on positive objects and also minimize errors that occur during the training process. The problems that arise next are how to overcome this so that a better knowledge is obtained. This study provides analysis results of the process of merging knowledge acquisition and its effect on the accuracy of object detection using the Viola-Jones method with the final result undetected object decrease 52.62% and object detected increase 23.78 %