Deteksi Obyek Manusia dengan Particle Swarm Optimization dan Pengklasifikasi Support Vector Machine
Abstract
ABSTRAK
Pada paper ini metode Particle Swarm Optimization (PSO) digunakan untuk medeteksi obyek manusia. Proses deteksi diawali dengan ektraksi fitur citra menggunakan metode Haar-wavelet. Proses pelatihan dengan pengklasifikasi Support Vector Machine (SVM). Tahap deteksi dengan PSO dilakukan menggunakan sekumpulan partikel yang tersebar di ruang pencarian dan mengoptimasi fungsi obyektif untuk mendeteksi obyek manusia. Hasil eksperimen pada database gambar sebanyak 50 citra berhasil medeteksi obyek manusia dengan tingkat akurasi 86%. Pendeteksian dengan metode PSO bisa digunakan untuk berbagai aplikasi keamanan dan pengawasan yang memerlukan waktu deteksi cepat.
Kata kunci : particle swarm optimization, deteksi obyek, support vector machine
ABSTRACT
In this paper Particle Swarm Optimization (PSO) method is used to detect human object. The detection process is iniatiated by extracting the image features using Haar-wavelet method. At the training phase the Support Vector Machine (SVM) Classifier is employed. The detection phase by the PSO method is using a group of particles which are spread over the search space and optimizing the objective function to detect human objects. The experiment results in the image database consist of 50 images is successfully detect objects with an accuracy rate of 86%. The Particle Swarm Optimization (PSO) detection can be applied in the surveillance and monitoring application which need a rapid detection.
Keywords : particle swarm optimization, object detection, support vector machine
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