scholarly journals Sistem Pengukur Kecepatan Kendaraan Berbasis Pengolahan Video

2015 ◽  
Vol 5 (2) ◽  
pp. 177
Author(s):  
Satrio Sani Sadewo ◽  
Raden Sumiharto ◽  
Ika Candradewi
Keyword(s):  
Light Intensity ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Average Speed ◽  
Speed Measurement ◽  
Measurement Validation ◽  
Model Region

AbstrakSistem pengukur kecepatan kendaraan berbasis pengolahan video ini merupakan salah satu sistem yang memanfaatkan sistem pengolahan citra digital sebagai pendeteksi kendaraan dan mengukur kecepatannya. Metode yang digunakan dalam sistem ini adalah background subtraction dengan algoritma Gaussian Mixture Model (GMM). Background subtraction akan memisahkan antara background dengan objek yang dideteksi, yaitu kendaraan. Koordinat titik tengah objek dijadikan sebagai nilai perpindahan objek dalam satuan piksel. Jarak sesungguhnya diukur dalam satuan meter. Jarak pada citra dibatasi dengan region of interest (ROI) sebesar 160 piksel. Setelah diperoleh waktu perpindahan tiap pikselnya maka nilai piksel/detik dikonversikan menjadi km/jam.Uji coba sistem dilakukan pada validasi kecepatan, pengukuran kecepatan, dan pengaruh intensitas cahaya. Proses validasi kecepatan menggunakan nilai kecepatan rata-rata 3 frame awal sebagai acuan untuk pengukuran kecepatan di frame berikutnya. Akurasi nilai kecepatan rata-rata 3 frame awal ini memberikan  persentase eror 1,92 % - 15,75 % sedangkan ketika validasi tersebut dilakukan pada pembacaan keseluruhan frame video menghasilkan rentang eror 1,21 % - 21,37 %. Sistem bekerja dengan baik pada kondisi pagi, siang, dan sore hari dengan rentang intensitas cahaya 600-1900 lux,  sedangkan pada malam hari dengan rentang intensitas cahaya 0-5 lux, sistem tidak bisa bekerja dengan baik. Kata kunci— pengolahan video, pengukuran kecepatan, background subtraction, gaussian mixture model, region of interest  AbstractThis system is implemented by digital image processing to detect the objects and measure the speed. This system using background subtraction method with Gaussian Mixture Model (GMM) algorithm. Background subtraction will separate background and detected objects. Coordinates of the objects midpoint used as the the object moving value in pixel. The actual distance also measured in meters where the distance is limited by region of interest (ROI). The ROI is 160 pixel. Having obtained the moving objects time from previous frame to current frame so the value of pixel/s can converted to km/h.System testing the measurement validation, calculate the speed after being validated, and the influence of light intensity. The speed validation process uses average speed of early three frames speed as the reference for the speed measurement in the next frame. The average speed accuracy of 3 frames early gives a percentage error about 1,92% - 15,75%. When validation is performed on the entire reading frame of video, it produces an error range 1,21% - 21,37%. The system works well in the morning, afternoon, and evening conditions with light intensity about 600-1900 lux. While at night with 0-5 lux light intensity range, the system can’t work properly. Keywords— video processing, speed measurement, background subtraction, gaussian mixture model, region of interest

Algorithm of Moving Vehicle Detection by Improved Gaussian Mixture Model Based on ROI

2015 ◽  
Vol 734 ◽  
pp. 463-467 ◽  
Author(s):  
Pan Pan Zhang ◽  
Chun Yang Mu ◽  
Xing Ma ◽  
Fu Lu Xu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Background Model ◽  
Morphological Filter ◽  
Improved Method ◽  
Moving Vehicle ◽  
Model Based ◽  
Moving Vehicle Detection

Detection of moving object is a hot topic in computer vision. Traditionally, it is detected for every pixel in whole image by Gaussian mixture background model, which may waste more time and space. In order to improving the computational efficiency, an advanced Gaussian mixture model based on Region of Interest was proposed. Firstly, the solution finds out the most probably region where the target may turn up. And then Gaussian mixture background model is built in this area. Finally, morphological filter algorithm is used for improving integrity of the detected targets. Results show that the improved method could have a more perfect detection but no more time increasing than typical method.

scholarly journals Towards the Development of Effective Video Segmentation Based on Skew Gaussian Mixture Model

2020 ◽  
Vol 9 (3) ◽  
pp. 1735-1745
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Video Segmentation ◽  
Gaussian Mixture ◽  
Dynamic Motion ◽  
Precise Analysis ◽  
Real Time Applications ◽  
Assessment Measures ◽  
Motion Images

video analysis has gained a exponential demand with its usage in security cameras and in most of the real time applications for monitoring the law order. In order to have a precise analysis background subtraction and foreground detection processed are generally considered in the most of the approaches. However ,to have a more precise output from the dynamic motion images, this article proposes a methodology based on skew Gaussian mixture model. The results are analyzed against the existing methods using quality assessment measures.

scholarly journals BMOG: boosted Gaussian Mixture Model with controlled complexity for background subtraction

2018 ◽  
Vol 21 (3) ◽  
pp. 641-654 ◽  
Author(s):  
Isabel Martins ◽  
Pedro Carvalho ◽  
Luís Corte-Real ◽  
José Luis Alba-Castro
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Gaussian Mixture

Background Subtraction Based on Gaussian Mixture Model

2013 ◽  
Vol 694-697 ◽  
pp. 2021-2026
Author(s):  
De Fang Liu ◽  
Ming Deng ◽  
Dai Mu Wang
Keyword(s):  
Probability Density ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Adaptive Algorithm ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Video Sequences ◽  
Recursive Equations ◽  
Gauss Mixture

According to the detection of moving objects in video sequences, the paper puts forward background subtraction based on Gauss mixture model. It analyzes the usual pixel-level approach, and to develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.

scholarly journals Research on Panoramic Stitching Algorithm of Lateral Cranial Sequence Images in Dental Multifunctional Cone Beam Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2200
Author(s):  
Junyuan Liu ◽  
Xi Li ◽  
Siwan Shen ◽  
Xiaoming Jiang ◽  
Wang Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Cone Beam Computed Tomography ◽  
Region Of Interest ◽  
Gaussian Mixture ◽  
Block Matching ◽  
Cone Beam ◽  
Fusion Algorithm ◽  
Panoramic Images

In the design of dental multifunctional Cone Beam Computed Tomography, the linear scanning strategy not only saves equipment cost, but also avoids the demand for patients to be repositioned when acquiring lateral cranial sequence images. In order to obtain panoramic images, we propose a local normalized cross-correlation stitching algorithm based on Gaussian Mixture Model. Firstly, the Block-Matching and 3D filtering algorithm is used to remove quantum and impulse noises according to the characteristics of X-ray images; Then, the segmentation of the irrelevant region and the extraction of the region of interest are performed by Gaussian Mixture Model; The locally normalized cross-relation is used to complete the registration with the multi-resolution strategy based on wavelet transform and Particle Swarm Optimization algorithm; Finally, image fusion is achieved by the weighted smoothing fusion algorithm. The experimental results show that the panoramic image obtained by this method has significant performance in both subjective vision and objective quality evaluation and can be applied to preoperative diagnosis of clinical dental deformity and postoperative effect evaluation.

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