• Title/Summary/Keyword: traffic detection system

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Development of a Safe Driving Management System (안전운전 관리시스템 개발)

  • Cho, Jun-Hee;Lee, Woon-Sung
    • Transactions of the Korean Society of Automotive Engineers
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    • v.15 no.1
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    • pp.71-77
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    • 2007
  • Dangerous driving is a major cause of traffic accidents in Korea. It becomes more serious for commercial vehicles due to higher fatality rates. The Safe Driving Management System (SDMS), developed in this research, is a comprehensive solution that monitors and stores driving conditions of vehicles, detects dangerous driving situations, and analyzes the results in real time. The Safe Driving Management System consists of a vehicle movement information controller, a dangerous driving detection algorithm and a vehicle movement data report and analysis program. The dangerous driving detection algorithm detects and classifies dangerous driving conditions into representative cases such as sudden acceleration, sudden braking, sudden lane change, and sudden turning. Both computer simulation and vehicle test have been conducted to develop and verify the algorithm. The Safe Driving Management System has been implemented on commercial buses to verify its reliability and objectivity. It is expected that the system can contribute to prevention of traffic accidents, systemization of safe driving management and reduction of commercial vehicle operation costs.

Protecting Accounting Information Systems using Machine Learning Based Intrusion Detection

  • Biswajit Panja
    • International Journal of Computer Science & Network Security
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    • v.24 no.5
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    • pp.111-118
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    • 2024
  • In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

Design and Implementation for Incident Detection Algorithm in Intelligent Transportation System (ITS 유고검지 시스템 설계 및 구현)

  • 전성주;백청호;최진탁
    • Journal of the Korea Computer Industry Society
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    • v.5 no.3
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    • pp.337-344
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    • 2004
  • ITS(Intelligent transportation system) provides users with realtime traffic information based on technologies such as advanced information & telecommunication, electronic control and transportation engineering. To operate efficient ITS, it is necessary to quickly identify and take actions for incidents(accidents, broken vehicles, public functions, traffic control, etc.). However, there have been few reliable incident detection algorithms developed so far. The algorithm presented in this study greatly resolved the problems in the existing incident detection algorithms, which determine incidents according to the input of constant values, by defining ranges based on the concept of pseudo level of service. With this improvement, operators can determine the incident detection parameters more accurately.

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A Scene-Specific Object Detection System Utilizing the Advantages of Fixed-Location Cameras

  • Jin Ho Lee;In Su Kim;Hector Acosta;Hyeong Bok Kim;Seung Won Lee;Soon Ki Jung
    • Journal of information and communication convergence engineering
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    • v.21 no.4
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    • pp.329-336
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    • 2023
  • This paper introduces an edge AI-based scene-specific object detection system for long-term traffic management, focusing on analyzing congestion and movement via cameras. It aims to balance fast processing and accuracy in traffic flow data analysis using edge computing. We adapt the YOLOv5 model, with four heads, to a scene-specific model that utilizes the fixed camera's scene-specific properties. This model selectively detects objects based on scale by blocking nodes, ensuring only objects of certain sizes are identified. A decision module then selects the most suitable object detector for each scene, enhancing inference speed without significant accuracy loss, as demonstrated in our experiments.

지역교차로 교통사고 자동검지시스템 개선을 위한 교차로 제 음향특성의 해석

  • Cho, Eul-Soo;Go, Young-Gwon;Kim, Jae-Yee
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2008.05a
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    • pp.789-792
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    • 2008
  • Actually, The present traffic accident detection system is subsisting limitation of accurate distinction under the crowded condition at intersection because the system depend upon mainly the image information at intersection and digital image processing techniques nearly all. To complement this insufficiency, this article aims to estimate the level of present technology and a realistic possibility by analyzing the acoustic characteristic of crash sound that we have to investigate for improvement of traffic accident detection rate at intersection. The skid sound of traffic accident is showed the special pattern at $1[kHz]{\sim}3[kHz}$ bandwidth when vehicles are almost never operated in and around intersection. Also, the frequency bandwidth of vehicle crash sound is showed sound pressure difference over 30[dB] higher than when there is no occurrence of traffic accident below 500[Hz].

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Estimation of Traffic Volume Using Deep Learning in Stereo CCTV Image (스테레오 CCTV 영상에서 딥러닝을 이용한 교통량 추정)

  • Seo, Hong Deok;Kim, Eui Myoung
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.38 no.3
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    • pp.269-279
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    • 2020
  • Traffic estimation mainly involves surveying equipment such as automatic vehicle classification, vehicle detection system, toll collection system, and personnel surveys through CCTV (Closed Circuit TeleVision), but this requires a lot of manpower and cost. In this study, we proposed a method of estimating traffic volume using deep learning and stereo CCTV to overcome the limitation of not detecting the entire vehicle in case of single CCTV. COCO (Common Objects in Context) dataset was used to train deep learning models to detect vehicles, and each vehicle was detected in left and right CCTV images in real time. Then, the vehicle that could not be detected from each image was additionally detected by using affine transformation to improve the accuracy of traffic volume. Experiments were conducted separately for the normal road environment and the case of weather conditions with fog. In the normal road environment, vehicle detection improved by 6.75% and 5.92% in left and right images, respectively, than in a single CCTV image. In addition, in the foggy road environment, vehicle detection was improved by 10.79% and 12.88% in the left and right images, respectively.

Improvement of A Preprocessing of Archived Traffic Data Collected by Expressway Vehicle Detection System (고속도로 차량검지기 이력자료 활용을 위한 전처리과정 개선)

  • Lee, Hwan-Pil;NamKoong, Seong;Kim, Soo-Hee;Kim, Jin
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.12 no.1
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    • pp.15-27
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    • 2013
  • While the vehicle detector is collected from a variety of information was mainly used as a real-time data. Recently scheme of application for archived traffic data has become increasingly important. In this background, this research were conducted on the improvement of the preprocessing for archived traffic data application. The purpose of improving specific preprocessing was reflect transportation phenomena by traffic data. As evaluation result, improvement preprocessing was close to the actual value than exist preprocessing.

Simulation of Traffic Signal Control with Adaptive Priority Order through Object Extraction in Images (영상에서 객체 추출을 통한 적응형 통행 우선순위 교통신호 제어 시뮬레이션)

  • Youn, Jae-Hong;Ji, Yoo-Kang
    • Journal of Korea Multimedia Society
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    • v.11 no.8
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    • pp.1051-1058
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    • 2008
  • The advancement of technology for image processing and communications makes it possible for current traffic signal controllers and vehicle detection technology to make both emergency vehicle preemption and transit priority strategies as a part of integrated system. Present]y traffic signal control in crosswalk is controlled by fixed signals. The signal control keeps regular signals traffic even with no traffic, when there is traffic, should wait until the signal is given. Waiting time causes the risk of traffic accidents and traffic congestion in accordance with signal violation. To help reduce the risk of accidents and congestion, this paper explains traffic signal control system for the adaptive priority order so that signal may be preferentially given in accordance with the situation of site through the object detect images.

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Traffic Signal Recognition System Based on Color and Time for Visually Impaired

  • P. Kamakshi
    • International Journal of Computer Science & Network Security
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    • v.23 no.4
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    • pp.48-54
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    • 2023
  • Nowadays, a blind man finds it very difficult to cross the roads. They should be very vigilant with every step they take. To resolve this problem, Convolutional Neural Networks(CNN) is a best method to analyse the data and automate the model without intervention of human being. In this work, a traffic signal recognition system is designed using CNN for the visually impaired. To provide a safe walking environment, a voice message is given according to light state and timer state at that instance. The developed model consists of two phases, in the first phase the CNN model is trained to classify different images captured from traffic signals. Common Objects in Context (COCO) labelled dataset is used, which includes images of different classes like traffic lights, bicycles, cars etc. The traffic light object will be detected using this labelled dataset with help of object detection model. The CNN model detects the color of the traffic light and timer displayed on the traffic image. In the second phase, from the detected color of the light and timer value a text message is generated and sent to the text-to-speech conversion model to make voice guidance for the blind person. The developed traffic light recognition model recognizes traffic light color and countdown timer displayed on the signal for safe signal crossing. The countdown timer displayed on the signal was not considered in existing models which is very useful. The proposed model has given accurate results in different scenarios when compared to other models.

Implementation and Performance Evaluation of High-Performance Intrusion Detection and Response System (고성능 침입탐지 및 대응 시스템의 구현 및 성능 평가)

  • Kim, Hyeong-Ju;Park, Dae-Chul
    • The KIPS Transactions:PartC
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    • v.11C no.2
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    • pp.157-162
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    • 2004
  • Recently, the growth of information infrastructure is getting fatter and faster. At the same time, the security accidents are increasing together. We have problem that do not handle traffic because we have the Intrusion Detection Systems in low speed environment. In order to overcome this, we need effective security analysis techniques that ran Processed data of high-capacity because high speed network environment. In this paper we proposed the Gigabit Intrusion Detection System for coordinated security function such as intrusion detection, response on the high speed network. We suggested the detection mechanism in high speed network environment that have pattern matching function based packet header and based packet data that is proceeded in system kernel area, we are shown that this mechanism was excellent until maximum 20 times than existing system in traffic processing performance.