Vehicles Detection Based on Background Modeling

Abstract

Background image subtraction algorithm is a common approach which detects moving objects in a video sequence by finding the significant difference between the video frames and the static background model. This paper presents a developed system which achieves vehicle detection by using background image subtraction algorithm based on blocks followed by deep learning data validation algorithm. The main idea is to segment the image into equal size blocks, to model the static reference background image (SRBI), by calculating the variance between each block pixels and each counterpart block pixels in the adjacent frame, the system implemented into four different methods: Absolute Difference, Image Entropy, Exclusive OR (XOR) and Discrete Cosine Transform (DCT). The experimental results showed that the DCT method has the highest vehicle detection accuracy.

Ekstrasi Fitur Daun Tembakau Berbasis Discrete Cosine Transform (DCT)

“…Tujuan dari penelitian ini adalah untuk mengeksplorasi koefisien Discrete Cosine Transform (DCT) dan fitur bentuk / morfologi untuk mendapatkan bentuk daun tembakau sehingga dapat digunakan untuk mengidentifikasi daun dengan akurasi tinggi yang didukung informasi lain seperti warna daun. Koefisien DCT dipilih berdasarkan kinerja yang baik dalam beberapa aplikasi diantaranya: klasifikasi protein [14], objek berbasis bentuk kendaraan [15] dan klasifikasi sidik jari [16]. Namun, beberapa fitur bentuk / morfologi lain dieksplorasi untuk mendukung koefisien DCT sehingga meningkatkan akurasi dalam mengidentifikasi daun tembakau.…”

A comprehensive review of vehicle detection using computer vision

“…For surveillance systems, one of the most popular types of devices are visible spectrum cameras, which have been extensively used for monitoring environments, events, activities, and people. Different studies in background-foreground segmentation [4][5][6][7] and object detection classification [8][9][10] have also attempted to automatically analyze image or video data from surveillance cameras.…”