Open AccessPENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER
Author(s) -
Kadek Oki Sanjaya,
Gede Indrawan,
Kadek Yota Ernanda Aryanto
Publication year - 2018
Publication title -
international journal of natural science and engineering
Language(s) - English
Resource type - Journals
eISSN - 2615-1383
pISSN - 2549-6395
DOI - 10.23887/ijnse.v1i3.12938
Subject(s) - haar like features , pixel , artificial intelligence , computer vision , adaboost , computer science , viola–jones object detection framework , classifier (uml) , object detection , pattern recognition (psychology) , cascading classifiers , cascade , haar , face detection , facial recognition system , random subspace method , wavelet , chemistry , chromatography
Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object