Automatic Measurement of Traffic State Parameters Based on Computer Vision for Intelligent Transportation Surveillance

2017 ◽  
Vol 32 (04) ◽  
pp. 1855003 ◽  
Author(s):  
Jianqiang Ren ◽  
Chunhong Zhang ◽  
Lingjuan Zhang ◽  
Ning Wang ◽  
Yue Feng
Keyword(s):  
Computer Vision ◽  
Traffic Congestion ◽  
Nearest Neighbor ◽  
Automatic Measurement ◽  
Intelligent Transportation ◽  
Vehicle Classification ◽  
Feature Points ◽  
K Nearest Neighbor ◽  
Spatial Image ◽  
Traffic State

Online automatic measurement of traffic state parameters has important significance for intelligent transportation surveillance. The video-based monitoring technology is widely studied today but the existing methods are not satisfactory at processing speed or accuracy, especially for traffic scenes with traffic congestion or complex road environments. Based on technologies of computer vision and pattern recognition, this paper proposes a novel measurement method that can detect multiple parameters of traffic flow and identify vehicle types from video sequence rapidly and accurately by combining feature points detection with foreground temporal-spatial image (FTSI) analysis. In this method, two virtual detection lines (VDLs) are first set in frame images. During working, vehicular feature points are extracted via the upstream-VDL and grouped in unit of vehicle based on their movement differences. Then, FTSI is accumulated from video frames via the downstream-VDL, and adhesive blobs of occlusion vehicles in FTSI are separated effectively based on feature point groups and projection histogram of blob pixels. At regular intervals, traffic parameters are calculated via statistical analysis of blobs and vehicles are classified via a K-nearest neighbor (KNN) classifier based on geometrical characteristics of their blobs. For vehicle classification, the distorted blobs of temporary stopped vehicles are corrected accurately based on the vehicular instantaneous speed on the downstream-VDL. Experiments show that the proposed method is efficient and practicable.

Automatic Vehicle Classification using Roadside LiDAR Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 153-164 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Yichen Zheng ◽  
Yongsheng Zhang ◽  
Bin Lv ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Vehicle Classification ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Height Profile ◽  
New Approach ◽  
Vehicle Trajectories ◽  
Different Types ◽  
Set Up ◽  
Neighbor Classification

This research presented a new approach for vehicle classification using roadside LiDAR sensor. Six features (one feature, object height profile, contains 10 sub-features) extracted from the vehicle trajectories were applied to distinguish different classes of vehicles. The vehicle classification aims to assign the objects into ten different types defined by FHWA. A database containing 1,056 manually marked samples and their corresponding pictures was provided for analysis. Those samples were collected at different scenarios (roads and intersections, different speed limits, day and night, different distance to LiDAR, etc.). Naïve Bayes, K-nearest neighbor classification, random forest (RF), and support vector machine were applied for vehicle classification. The results showed that the performance of different methods varied by class. RF has the highest overall accuracy among those investigated methods. Some types were merged together to serve different types of users, which can also improve the accuracy of vehicle classification. The validation indicated that the distance between the object and the roadside LiDAR can influence the accuracy. This research also provided the distribution of the overall accuracy of RF along the distance to LiDAR. For the VLP-16 LiDAR, to achieve an accuracy of 91.98%, the distance between the object and LiDAR should be less than 30 ft. Users can set up the location of the roadside LiDAR based on their own requirements of the classification accuracy.

Automatic Defect Detection and Classification of Terminals in a Bussed Electrical Center Using Computer Vision

2016 ◽  
pp. 241-266
Author(s):  
Osslan Osiris Vergara Villegas ◽  
Vianey Guadalupe Cruz Sánchez ◽  
Humberto de Jesús Ochoa Domínguez ◽  
Jorge Luis García-Alcaraz ◽  
Ricardo Rodriguez Jorge
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Nearest Neighbor ◽  
Region Of Interest ◽  
Morphological Operation ◽  
K Nearest Neighbor ◽  
Electrical Center ◽  
Salt And Pepper ◽  
Intelligent Computer

In this chapter, an intelligent Computer Vision (CV) system, for the automatic defect detection and classification of the terminals in a Bussed Electrical Center (BEC) is presented. The system is able to detect and classify three types of defects in a set of the seven lower pairs of terminals of a BEC namely: a) twisted; b) damaged and c) missed. First, an environment to acquire a total of 56 training and test images was created. After that, the image preprocessing is performed by defining a Region Of Interest (ROI) followed by a binarization and a morphological operation to remove small objects. Then, the segmentation stage is computed resulting in a set of 12-14 labeled zones. A vector of 56 features is extracted for each image containing information of area, centroid and diameter of all terminals segmented. Finally, the classification is performed using a K-Nearest Neighbor (KNN) algorithm. Experimental results on 28 BEC images have shown an accuracy of 92.8% of the proposed system, allowing changes in brightness, contrast and salt and pepper noise.

scholarly journals Classification of Mango Fruit Quality Based on Texture Characteristics of GLCM (Gray Level Co-Occurrence Matrices) with Algorithm K-NN (K-Nearest Neighbors)

2019 ◽  
Vol 20 (1) ◽  
pp. 31
Author(s):  
Wahyu Wijaya Widiyanto ◽  
Eko Purwanto ◽  
Kusrini Kusrini
Keyword(s):  
Computer Vision ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Gray Level ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Mango Fruit ◽  
K Nearest Neighbors ◽  
Texture Characteristics

Proses klasifikasi kualitas mutu buah mangga dengan cara konvensional menggunakan mata manusia memiliki kelemahan di antaranya membutuhkan tenaga lebih banyak untuk memilah, anggapan mutu kualitas buah mangga antar manusia yang berbeda, tingkat konsistensi manusia dalam menilai kualitas mutu buah mangga yang tidak menjamin valid karena manusia dapat mengalami kelelahan. Penelitian ini bertujuan untuk klasifikasi kualitas mutu buah mangga ke dalam tiga kelas mutu yaitu kelas Super, A, dan B dengan computer vision dan algoritma k-Nearest Neighbor. Hasil pengujian menggunakan jumlah k tetangga 9 menunjukan tingkat akurasi sebesar 88,88%.Kata-kata kunci— Klasifikasi, GLCM, K-Nearest Neighbour, Mangga

scholarly journals Highway Travel Time Prediction Using Sparse Tensor Completion Tactics and K-Nearest Neighbor Pattern Matching Method

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jiandong Zhao ◽  
Yuan Gao ◽  
Jinjin Tang ◽  
Lingxi Zhu ◽  
Jiaqi Ma
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Nearest Neighbor ◽  
Sensor Nodes ◽  
Tensor Completion ◽  
Volume Data ◽  
K Nearest Neighbor ◽  
Travel Time Prediction ◽  
Data Sparseness ◽  
Time Prediction

Remote transportation microwave sensor (RTMS) technology is being promoted for China’s highways. The distance is about 2 to 5 km between RTMSs, which leads to missing data and data sparseness problems. These two problems seriously restrict the accuracy of travel time prediction. Aiming at the data-missing problem, based on traffic multimode characteristics, a tensor completion method is proposed to recover the lost RTMS speed and volume data. Aiming at the data sparseness problem, virtual sensor nodes are set up between real RTMS nodes, and the two-dimensional linear interpolation and piecewise method are applied to estimate the average travel time between two nodes. Next, compared with the traditional K-nearest neighbor method, an optimal KNN method is proposed for travel time prediction. optimization is made in three aspects. Firstly, the three original state vectors, that is, speed, volume, and time of the day, are subdivided into seven periods. Secondly, the traffic congestion level is added as a new state vector. Thirdly, the cross-validation method is used to calibrate the K value to improve the adaptability of the KNN algorithm. Based on the data collected from Jinggangao highway, all the algorithms are validated. The results show that the proposed method can improve data quality and prediction precision of travel time.

scholarly journals Implementasi K-Nearest Neighbor untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB

2020 ◽  
Vol 7 (6) ◽  
pp. 1129
Author(s):  
Lia Farokhah
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Nearest Neighbor ◽  
Nearest Neighbour ◽  
K Nearest Neighbor ◽  
Color Features ◽  
Knn Classification ◽  
Total Data ◽  
Shape Characteristics

<p class="Abstrak">Era computer vision merupakan era dimana komputer dilatih untuk bisa melihat, mengidentifikasi dan mengklasifikasi seperti kecerdasan manusia. Algoritma klasifikasi berkembang dari yang paling sederhana seperti K-Nearest Neighbor (KNN) sampai Convolutional Neural Networks. KNN merupakan algoritma klasifikasi yang paling sederhana dalam mengklasifikasikan sebuah gambar kedalam sebuah label. Metode ini mudah dipahami dibandingkan metode lain karena mengklasifikasikan berdasarkan jarak terdekat dengan objek lain (tetangga). Tujuan penelitian ini untuk membuktikan kelemahan metode KNN dan ekstraksi fitur warna RGB dengan karakteristik tertentu. Percobaan pertama dilakukan terhadap dua objek dengan kemiripan bentuk tetapi dengan  warna yang  mencolok di salah satu sisi objek. Percobaan kedua terhadap dua objek yang memiliki perbedaan karakteristik bentuk meskipun memiliki kemiripan warna. Empat objek tersebut adalah bunga coltsfoot, daisy, dandelion dan matahari. Total data dalam dataset adalah 360 data. Dataset memiliki tantangan variasi sudut pandang, penerangan, dan  gangguan dalam latar. Hasil menunjukkan bahwa kolaborasi metode klasifikasi KNN dengan ekstraksi fitur warna RGB memiliki kelemahan terhadap percobaan pertama dengan akurasi 50-60% pada K=5. Percobaan kedua memiliki akurasi sekitar 90-100% pada K=5. Peningkatan akurasi, precision dan recall terjadi ketika menaikkan jumlah K yaitu dari K=1menjadi K=3 dan K=5.</p><p><strong>Kata kunci</strong>: k-nearest neighbour, RGB, kelemahan, kemiripan, bunga</p><p class="Judul2" align="left"> </p><p class="Judul2"> </p><p class="Judul"><em>IMPLEMENTATION OF K-NEAREST NEIGHBOR FOR FLOWER CLASSIFICATION WITH EXTRACTION OF RGB COLOR FEATURES</em></p><p class="Judul"><em>The era of computer vision is an era where computers are trained to be able to see, identify and classify as human intelligence. Classification algorithms develop from the simplest such as K-Nearest Neighbor (KNN) to Convolutional Neural Networks. KNN is the simplest classification algorithm in classifying an image into a label. This method is easier to understand than other methods because it classifies based on the closest distance to other objects (neighbors). The purpose of this research is to prove the weakness of the KNN method and the extraction of RGB color features for specific characteristics. The first  experiment on two objects with similar shape but with sharp color on one side of the object. The second experiment is done on two objects that have different shape characteristics even having a similar colors. The four objects are coltsfoot, daisy, dandelion and sunflower. Total data in the dataset is 360 data. The dataset has the challenge of varying viewpoints, lighting and background noise. The results show that the collaboration of the KNN classification method with RGB color feature extraction has weakness in the first experiment with the level of accuracy about 50-60% at K = 5. The second experiment has an accuracy of around 90-100% at K = 5. Increased accuracy, precision and recall occur when increasing the amount of K, from K = 1 to K = 3 and K = 5.</em></p><p class="Judul2"> </p><p class="Judul2" align="left"> </p><strong>Keywords</strong>: <em>k-nearest neighbour</em>, RGB, <em>weakness, similar, flower</em>

scholarly journals Identifikasi Penyakit Daun Jeruk Siam Menggunakan K-Nearest Neighbor

2021 ◽  
Vol 1 (2) ◽  
pp. 133-140
Author(s):  
Rifqi Hakim Ariesdianto ◽  
Zilvanhisna Emka Fitri ◽  
Abdul Madjid ◽  
Arizal Mujibtamala Nanda Imron
Keyword(s):  
Computer Vision ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Xanthomonas Axonopodis

Jeruk siam adalah salah satu jeruk local yang mempunyai nilai jual yang tinggi di Indonesia. Tahun 2020, tingkat produksi jeruk siam mengalami penurunan menjadi 712.585 ton di Jawa Timur. Salah satu faktor utama yang menyebabkan menurunnya tingkat produksi jeruk siam yaitu serangan penyakit pada daun jeruk siam. Dua penyakit yang sering menyerang daun jeruk siam adalah penyakit kanker yang disebabkan oleh patogen Xanthomonas axonopodis pv.citri dan penyakit ulat peliang. Selama ini, pengamatan pada penyakit daun jeruk siam dilakukan secara manual menggunakan mata sehingga penentuan penyakit tersebut bersifat subyektif. Untuk mengatasi masalah tersebut dibuatlah sistem otomatis identifikasi daun jeruk siam sehat dan daun jeruk siam terserang penyakit dengan bantuan teknik computer vision. Tahapan penelitian yaitu pengumpulan citra daun jeruk, konversi warna, ekstraksi fitur warna dan tekstur serta klasifikasi K-Nearest Neighbor (KNN). Parameter fitur yang digunakan yaitu fitur warna GB, fitur tekstur (ASM, entropi dan kontras). Metode KNN mampu mengklasifikasi dan mengidentifikasi penyakit daun jeruk siam dengan akurasi sebesar 70% dengan variasi nilai K = 21.

scholarly journals A Special Event-Based K-Nearest Neighbor Model for Short-Term Traffic State Prediction

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 81717-81729 ◽  
Author(s):  
Haiyang Yu ◽  
Nan Ji ◽  
Yilong Ren ◽  
Can Yang
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Short Term ◽  
Special Event ◽  
Traffic State ◽  
State Prediction ◽  
Event Based

scholarly journals IMAGE MOSAIC ALGORITHM BASED ON PCA-ORB FEATURE MATCHING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 83-89
Author(s):  
J. T. Zhu ◽  
C. F. Gong ◽  
M. X. Zhao ◽  
L. Wang ◽  
Y. Luo
Keyword(s):  
Principal Component Analysis ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Descriptor ◽  
Image Stitching ◽  
Feature Points ◽  
K Nearest Neighbor ◽  
Fusion Algorithm

Abstract. In the process of image stitching, the ORB (Oriented FAST and Rotated BRIEF) algorithm lacks the characteristics of scale invariance and high mismatch rate. A principal component invariant feature transform (PCA-ORB, Principal Component Analysis- Oriented) is proposed. FAST and Rotated BRIEF) image stitching method. Firstly, the ORB algorithm is used to optimize the feature points to obtain the feature points with uniform distribution. Secondly, the principal component analysis (PCA) method can reduce the dimension of the traditional ORB feature descriptor and reduce the complexity of the feature point descriptor data. Thirdly, KNN (K-Nearest Neighbor) is used, and the k-nearest neighbor algorithm performs roughly matching on the feature points after dimensionality reduction. Then the random matching consistency algorithm (RANSAC, Random Sample Consensus) is used to remove the mismatched points. Finally, the fading and fading fusion algorithm is used to fuse the images. In 8 sets of simulation experiments, the image stitching speed is improved relative to the PCA-SIFT algorithm. The experimental results show that the proposed algorithm improves the image stitching speed under the premise of ensuring the quality of stitching, and can play a role in fast, real-time and large-scale applications, which are conducive to image fusion.

scholarly journals Artificial Intelligence in Automated Sorting in Trash Recycling

2018 ◽  
Author(s):  
Bernardo S. Costa ◽  
Aiko C. S. Bernardes ◽  
Julia V. A. Pereira ◽  
Vitoria H. Zampa ◽  
Vitoria A. Pereira ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Image Database ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Process Waste

A computer vision approach to classify garbage into recycling categories could be an efficient way to process waste. This project aims to take garbage waste images and classify them into four classes: glass, paper, metal and, plastic. We use a garbage image database that contains around 400 images for each class. The models used in the experiments are Pre-trained VGG-16 (VGG16), AlexNet, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and, Random Forest (RF). Experiments showed that our models reached accuracy around 93%.

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