scholarly journals Sistem Penegakan Speed Bump Berdasarkan Kecepatan Kendaraan yang Diklasifikasikan Haar Cascade Classifier

2019 ◽  
Vol 7 (1) ◽  
pp. 12-18
Author(s):  
Muhammad Zulfikri ◽  
Erni Yudaningtyas ◽  
Rahmadwati Rahmadwati
Keyword(s):  
High Speed ◽  
Road Traffic ◽  
Detection System ◽  
Warning System ◽  
Detection Accuracy ◽  
Cascade Classifier ◽  
Car Detection ◽  
Speed Measurement ◽  
Haar Cascade ◽  
Safety Limit

Driving at high speed is among the frequent causes of accidents. In this research, a warning system was developed to warn drivers when their speed beyond the safety limit. Haar cascade classifier was proposed for the detection system which comprises Haar features, integral image, AdaBoost learning, and cascade classifier. The system was implemented using Python OpenCV library and evaluated on road traffic video collected in one way traffic. As a result, the proposed method yields 97.92% of car detection accuracy in daylight and MSE of 2.88 in speed measurement.

scholarly journals PENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER

2018 ◽  
Vol 1 (3) ◽  
pp. 92
Author(s):  
Kadek Oki Sanjaya ◽  
Gede Indrawan ◽  
Kadek Yota Ernanda Aryanto
Keyword(s):  
Image Processing ◽  
Detection System ◽  
High Accuracy ◽  
Special Study ◽  
Cascade Classifier ◽  
Integral Image ◽  
Main Method ◽  
Average Accuracy ◽  
Haar Features ◽  
Haar Cascade

Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object

An Enhanced Approach for Number Plate Detection and Recognition

2020 ◽  
Vol 13 ◽  
Author(s):  
Diksha Kurchaniya ◽  
Mohd. Aquib Ansari ◽  
Durga Patel
Keyword(s):  
Character Recognition ◽  
High Speed ◽  
Detection System ◽  
Wiener Filter ◽  
Noise Removal ◽  
Motor Vehicles ◽  
Detection Accuracy ◽  
Morphological Operations ◽  
Wide Range ◽  
High Speed Vehicle

Introduction: The number of vehicles is increasing day by day in our life. The vehicle may violate traffic rules and cause accidents. The automatic number plate detection system (ANPR) plays a significant role to identify these vehicles. Number plate detection is very difficult sometimes because each country has its own format for representing the number plate and font types and sizes may also vary for different vehicles. The number of ANPR systems is available nowadays but still, it is a big problem to detect the number plate correctly in various scenarios like high-speed vehicle, number plate language, etc. Methods: In the development of this method, we mainly used wiener filter for noise removal, morphological operations for number plate localization, connected component algorithm for character segmentation, and template based matching for character recognition. Results: Our proposed methodology is providing promising results in terms of detection accuracy. Discussion: The automatic number plate detection system (ANPR) has wide range of applications because the license number is the crucial, commonly putative and essential identifier of motor vehicles. These applications include ticketless parking fee management, parking access automation, car theft prevention, security guide assistance, Motorway Road Tolling, Border Control, Journey Time Measurement, Law Enforcement and many more. Conclusion: In this paper, an enhanced approach of automatic number plate detection system is proposed using some different techniques which not only detect the number plate of the vehicle but also recognize each character present in the detected number plate image.

Intrusion Detection Systems Alerts Reduction

2018 ◽  
pp. 255-275 ◽  
Author(s):  
Aymen Akremi ◽  
Hassen Sallay ◽  
Mohsen Rouached
Keyword(s):  
Intrusion Detection ◽  
High Speed ◽  
Detection System ◽  
Search Space ◽  
Relevant Information ◽  
Intrusion Detection Systems ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Rules Set ◽  
Intrusion Alerts

Investigators search usually for any kind of events related directly to an investigation case to both limit the search space and propose new hypotheses about the suspect. Intrusion detection system (IDS) provide relevant information to the forensics experts since it detects the attacks and gathers automatically several pertinent features of the network in the attack moment. Thus, IDS should be very effective in term of detection accuracy of new unknown attacks signatures, and without generating huge number of false alerts in high speed networks. This tradeoff between keeping high detection accuracy without generating false alerts is today a big challenge. As an effort to deal with false alerts generation, the authors propose new intrusion alert classifier, named Alert Miner (AM), to classify efficiently in near real-time the intrusion alerts in HSN. AM uses an outlier detection technique based on an adaptive deduced association rules set to classify the alerts automatically and without human assistance.

A Detection System of Tool Condition Based on Image Processing Technology

2012 ◽  
Vol 510 ◽  
pp. 375-379
Author(s):  
Xing Yuan Kou ◽  
Chen Sheng Wang ◽  
Lei Li
Keyword(s):  
Image Processing ◽  
High Speed ◽  
Detection System ◽  
Digital Processing ◽  
Processing Technology ◽  
Detection Accuracy ◽  
Size Change ◽  
Tool Condition ◽  
Image Processing Technology ◽  
Processing Solution

This paper focused on introducing a real-time detection system of tool condition based on image processing technology and determined final algorithm after analyzing several different operators of edge detection. Firstly, the system will complete an acquisition of tool image, and transfer it to the computer. Then the image will be processed by some digital processing algorithms. At last, we can get the tool wear according to the size change of a tool. The result of this study showed that the detection system and its image processing solution could give a desired result. The detection accuracy could reach macron level, and the degree of automation could be improved too. So the system can be used to better meet the needs of high precision and high-speed in modern production.

scholarly journals Comparison of Signal-Analysis Techniques for Seismic Detection System for High-Speed Train Data: Effect of Bridge Structures

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6805
Author(s):  
Mintaek Yoo ◽  
Jae Sang Moon
Keyword(s):  
High Speed ◽  
Detection System ◽  
Warning System ◽  
Data Sets ◽  
Acceleration Data ◽  
Bridge Structures ◽  
High Speed Trains ◽  
Analysis System ◽  
Data Analysis System ◽  
Short Time

This study evaluated the earthquake warning system for high-speed trains using onboard accelerometers instead of expensive seismometers. Onboard accelerometers measure the train data additional to the earthquake acceleration. The measured earthquake acceleration could also be modified by railroad-supporting bridges. To develop the data analysis system, the virtual onboard data sets are synthesized using the train acceleration data and earthquake data. Not only the earthquake acceleration data but also the earthquake responses of bridges are used for the virtual onboard data synthesis. For the analysis of synthesized data, the short-time Fourier Transform (STFT), the wavelet transform (WT), and Wigner–Ville Distribution (WVD) methods have been compared. Results show that WVD provides the best detection performance while the computational costs are large.

scholarly journals Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed

2021 ◽  
Vol 11 (2) ◽  
pp. 897-910
Author(s):  
K. Pavani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Traffic Density ◽  
Cascade Algorithm ◽  
Real Time Traffic ◽  
Accuracy And Precision ◽  
Haar Cascade ◽  
Better Than

Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.

scholarly journals Detection of Iris Localization in Facial Images Using Haar Cascade Circular Hough Transform

2020 ◽  
Vol 55 (4) ◽  
Author(s):  
Haider Shamil ◽  
Bassam Al Kindy ◽  
Amel H. Abbas
Keyword(s):  
Face Detection ◽  
Hough Transform ◽  
High Speed ◽  
Suggested Method ◽  
Cascade Classifier ◽  
Adaboost Algorithm ◽  
Circular Hough Transform ◽  
Haar Cascade ◽  
Iris Center ◽  
Facial Images

In numerous science applications, face detection and iris extraction have been recognized as crucial stages by getting more consideration among researchers as it has an important job. This paper presents an automatic detection method of the iris and its center detection by applying the Haar Cascade Classifier and the Circular Hough Transform algorithm. The suggested method is divided into two primary methodologies: face recognition utilizes the Haar Cascade Classifier and iris extraction using the Hough Transform. The system detects the face from a set of facial images using an Impa-faced dataset. The improved AdaBoost algorithm constructs a cascaded classifier for face detection. Then, by applying the Haar Cascade to obtain an eye pair region and a Hough transform for iris detection by extracting Haar features. Finally, the improved circular Hough transform algorithm locates the iris center. The experimental results of the suggested method show a high-speed, robust ability to acquire the coordinates of the iris center accurately under various illumination changes on different states of human images. The overall accuracy for locating the iris center was 98.75%.

scholarly journals DETEKSI ALAT PELINDUNG KEPALA (HELM) MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER

JOUTICA ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 189
Author(s):  
Sugianto Sugianto
Keyword(s):  
Detection System ◽  
Training Data ◽  
Cascade Classifier ◽  
Protective Gear ◽  
Detection Process ◽  
Safety And Health ◽  
Haar Feature ◽  
Haar Cascade ◽  
Feature Values ◽  
Testing Group

Use of protective gear helmet head is often considered unimportant and trivial by workers. Whereas the use of protective headgear helmet is very important and affect the safety and health of workers. Kedisiplina workers to use protective gear head is still low so that the risk of accidents that could endanger workers large enough. In this research aims to detect protective equipment head helmet on video. In this study, the method used is the Haar Cascade Classifier. The system consists of two main processes, namely the process of training data and the detection process. This method of training process has four main processes, haar-like feature, integral image, no-boost and cascade classifier. Haar-like feature is a collection of special features presented the head, face and helmet. Citra is how to quickly calculate integrals haar feature. While no-boost are statistically weighted feature values are obtained and filtered using a cascade classifier. The detection process in this study there are two processes, the first detection process whether human or not, if the result of human detected will continue the process of detection of whether to use a helmet or not. Detection system testing is done individually using helmet colors red, blue and yellow. It obtained accuracy rate of 92%, while the testing group obtained the degree of accuracy of 71%.

Based on the Human Eye Fatigue Detection System of Recognition and FPGA Technology

2014 ◽  
Vol 672-674 ◽  
pp. 1995-1998 ◽  
Author(s):  
Jun Wang ◽  
Qiang Liu ◽  
Hang Zhao
Keyword(s):  
High Speed ◽  
Detection System ◽  
A Priori ◽  
Color Space ◽  
Gaussian Model ◽  
Region Growing ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Threshold Detection ◽  
Human Eye

This paper introduces the field programmable gate array (fpga) application in high-speed visual inspection system.ALTERA EP1K30QC208-2 are used in the system for system calculation and control of the core, to perform high-speed real-time visual detection algorithm, this paper adopts a yawning based on eye closure and to detect driver fatigue, the method of in YCr, Cb in the color space using gaussian model skin detection of human face area, in the face of a gray binarization figure using a priori knowledge of facial features geometry in rough positioning the human eye, eye contour are obtained by region growing and morphological operation and calculation of the closure of the eye;Best threshold detection lips when using lip color roughly locate the lips, on the basis of accurate positioning lips by face grey value characteristics, and then through the mouth level to determine whether a driver yawn;Based on the two characteristics of driving fatigue, experiments show that this system detection speed, excellent versatility, and can improve the detection accuracy.

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