Vehicle Detection Method for Intelligent Vehicle at Night Time Based on Video and Laser Information

2017 ◽  
Vol 32 (04) ◽  
pp. 1850009 ◽  
Author(s):  
Rong-Hui Zhang ◽  
Feng You ◽  
Fang Chen ◽  
Wen-Qiang He
Keyword(s):  
Gabor Filter ◽  
Region Growing ◽  
Vehicle Detection ◽  
Research Field ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Night Time ◽  
Video Images ◽  
Detection Technology ◽  
Svm Algorithm

Front vehicle detection technology is one of the hot spots in the advanced driver assistance system research field. This paper puts forward a method for front vehicles detection based on video-and-laser-information at night. First of all, video images and laser data are pre-processed with the region growing and threshold area expunction algorithm. Then, the features of front vehicles are extracted by use of a Gabor filter based on the uncertainty principle, and the distances to front vehicles are obtained through laser point cloud. Finally, front vehicles are automatically classified during identification with the improved sequential minimal optimization algorithm, which was based on the support vector machine (SVM) algorithm. According to the experiment results, the method proposed by this text is effective and it is reliable to identify vehicles in front of intelligent vehicles at night.

scholarly journals Front-Vehicle Detection in Video Images Based on Temporal and Spatial Characteristics

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1728 ◽  
Author(s):  
Bo Yang ◽  
Sheng Zhang ◽  
Yan Tian ◽  
Bijun Li
Keyword(s):  
Motion Vector ◽  
Vehicle Detection ◽  
Feature Points ◽  
Spatial Characteristics ◽  
Video Images ◽  
Detection Technology ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics ◽  
Spatial And Temporal Characteristics ◽  
Background Feature

Assisted driving and unmanned driving have been areas of focus for both industry and academia. Front-vehicle detection technology, a key component of both types of driving, has also attracted great interest from researchers. In this paper, to achieve front-vehicle detection in unmanned or assisted driving, a vision-based, efficient, and fast front-vehicle detection method based on the spatial and temporal characteristics of the front vehicle is proposed. First, a method to extract the motion vector of the front vehicle is put forward based on Oriented FAST and Rotated BRIEF (ORB) and the spatial position constraint. Then, by analyzing the differences between the motion vectors of the vehicle and those of the background, feature points of the vehicle are extracted. Finally, a feature-point clustering method based on a combination of temporal and spatial characteristics are applied to realize front-vehicle detection. The effectiveness of the proposed algorithm is verified using a large number of videos.

Research on the Vehicle Detection Technology Based on Improved Burendra Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1733-1736
Author(s):  
Qiang Li ◽  
Dong Shen ◽  
Xiao Kang Wu
Keyword(s):  
Light Intensity ◽  
Real Time ◽  
Difference Method ◽  
Vehicle Detection ◽  
Moving Targets ◽  
Time Performance ◽  
Video Images ◽  
Detection Technology ◽  
Background Difference

To the background establishment in background difference method, a method is proposed to detect moving targets based on the improved Burendra algorithm. This method can accurately detect moving targets in the video images, meanwhile it can respond to the changes of background and light intensity in the scene in time, being with good real-time performance and robustness.

scholarly journals Approche de sélection d’attributs pour la classification basée sur l’algorithme RFE-SVM

2014 ◽  
Vol Volume 17 - 2014 - Special... ◽  
Author(s):  
Yahya Slimani ◽  
Mohamed Amir Essegir ◽  
Mouhamadou Lamine Samb ◽  
Fodé Camara ◽  
Samba Ndiaye
Keyword(s):  
Optimization Methods ◽  
Research Field ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Hybrid Approaches ◽  
Alternative Approach ◽  
Svm Algorithm ◽  
International Audience ◽  
Selection For ◽  
Active Research

International audience The feature selection for classification is a very active research field in data mining and optimization. Its combinatorial nature requires the development of specific techniques (such as filters, wrappers, genetic algorithms, and so on) or hybrid approaches combining several optimization methods. In this context, the support vector machine recursive feature elimination (SVM-RFE), is distinguished as one of the most effective methods. However, the RFE-SVM algorithm is a greedy method that only hopes to find the best possible combination for classification. To overcome this limitation, we propose an alternative approach with the aim to combine the RFE-SVM algorithm with local search operators based on operational research and artificial intelligence.

Study on Traffic Sign Recognition by Optimized Lenet-5 Algorithm

2019 ◽  
Vol 34 (01) ◽  
pp. 2055003 ◽  
Author(s):  
Chuanwei Zhang ◽  
Xiangyang Yue ◽  
Rui Wang ◽  
Niuniu Li ◽  
Yupeng Ding
Keyword(s):  
Real Time ◽  
Recognition Accuracy ◽  
Gabor Filter ◽  
Intelligent Vehicles ◽  
Support Vector ◽  
Traffic Sign Recognition ◽  
Noise Elimination ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Time Accuracy

Traffic sign recognition (TSR) is a key technology of intelligent vehicles, which is based on visual perception for road information. In view of the fact that the traditional computer vision identification technology cannot meet the requirements of real-time accuracy, the TSR algorithm has been proposed on the basis of improved Lenet-5 algorithm. Firstly, we performed picture noise elimination and image enhancement on selected traffic sign images. Secondly, we used Gabor filter kernel in the convolution layer for convolution operation. In the convolution process, we added normalization layer Batch Normality (BN) after each convolution layer and reduced the data dimension. In the down-sampling layer, we replaced Sigmoid with the Relu activator. Finally, we selected the expanded GTSRB traffic sign database for the comparison experiment on the Caff platform. The experimental results showed that the proposed improved Lenet-5 network test set had the recognition accuracy of 96%, which was better than the method that combined Gabor with Support Vector Machine (SVM) in terms of recognition accuracy and real-time performance.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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