Abstract
Road safety monitoring systems are developing at this time. The transportation sector is the object of research that continues to be developed and is always an interesting topic. Not only for security purposes and for statistical purposes for the road widening process that supports road user infrastructure, but the detection system is also useful for sales marketing statistics. In this research, propose a vehicle detection system that is useful for widening roads in a certain area or area so that it can reduce traffic congestion and accident rates. The proposed Gaussian Mixture Model method has several weaknesses, such as errors in background substitution with vehicles and failing to distribute the background with vehicle shadows. However, using morphological operations can overcome these problems. The results show a fairly good level of accuracy from the proposed method. It is only less effective when using video objects with poor lighting or at night because in the blob analysis process the detected vehicle objects do not match the actual object. But if the traffic flow is smooth and unidirectional, the proposed method is still acceptable.
Highlights
Background substractionMerupakan awal dimana objek bergerak tersegmentasi dari background
The results show a fairly good level of accuracy from the proposed method
Chin Fan, “Vehicle Detection Using Normalized Color and Edge Map”, IEEE Transactions on Image Processing. ,Vol., No.3, pp. 850 – 864, March.2007
Summary
Kebutuhan sistem pemantauan diberbagai bidang saat ini sangat dibutuhan. Sistem pemantauan ini bertujuan untuk produktifitas maupun keamanan suatu sector. Pendeteksian menggunakann sensor magnet Anisotropic Magnetoresistive Sensor (AMR) dapat memberikan hasil yang akurat, proses instalasinya mudah sehingga beberapa penelitian dan survey kendaraan banyak yang menggunakann metode ini [17]. Untuk itu kami menerapkan metode Gaussian Mixture Model (GMM) yang digunakan sebagai pemodelan warna background dari tiap piksel yang memiliki peran penting terhadap restoriasi gambar [8]. Hasil klasifikasi dengan menggunakann metode gaussian mixture model (GMM) terbilang cukup tinggi [3], namun membutuhkan waktu komputasi yang lambat karena proses menyimpan inversi matriks yang cukup banyak [8]. Deteksi blob digunakan untuk mengetahui posisi objek kendaraan sebelum dilakukan perhitungan jumlah objek pada suatu citra dari sebuah video. Hasil operasi morfologi dapat memperhalus distribusi foreground yang dapat memperkecil bentuk dimensi kendaraan sehingga dapat mendekati bentuk sebenarnya dan untuk mengurangi shake atau gerakan kecil yang tidak perlu disegmentasi
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