• Journal of Multimedia Information System
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• v.5 no.1
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• pp.9-14
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• 2018

A road recognition system or Lane departure warning system is an early stage technology that has been commercialized as early as 10 years but can be optional and used as an expensive premium vehicle, with a very small number of users. Since the system installed on a vehicle should not be error prone and operate reliably, the introduction of robust feature extraction and tracking techniques requires the development of algorithms that can provide reliable information. In this paper, we investigate and analyze various real-time road detection algorithms based on color information. Through these analyses, we would like to suggest the algorithms that are actually applicable.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.219-220
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• 2007

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.161-166
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• 2021

The term parking management system usually refers to the custom built hardware intensive systems installed in building and malls. However, there are many places where such expensive solutions cannot be installed due to various reasons, like cost and urgent/temporary setup requirements. This project focuses on developing a parking management system based on image processing to detect vacant parking slot in an area where automated systems are not installed. Camera images of the parking area are subjected to image processing algorithm which marks virtual slots in the area and extracts occupancy information to guide the incoming drivers about availability and position of vacant spaces. The application consists of two interfaces: one for the guidance of the incoming drivers and the other one for the administrator. The later interface also informs the administrator if a car is not parked properly in the virtual slot. This parking system would reduce the stress and time wastage associated with car parking and would make the management of such areas less costly.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.51-62
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• 2008

An innovative feature of this study is to propose a methodology for evaluating safety performance in real time based on vehicle trajectory data extracted from video images. The essence of evaluating safety performance is to capture unsafe car-following and lane-changing events generated by individual vehicles traveling within video surveillance area. The proposed methodology derived three indices including real-time safety index(RSI) based on the concept of safe stopping distance, time-to-collision(TTC), and the collision energy based on the conservation of momentum. It is believed that outcomes would be greatly utilized in developing a new generation of video images processing(VIP) based traffic detection systems capable of producing safety performance measurements. Relevant technical challenges for such detection systems are also discussed.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.93-99
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• 2020

A majority of car crash is affected by careless driving that causes extensive economic and social costs, as well as injuries and fatalities. Thus, the research of precise crash detection systems is very significant issues in automotive safety. A lot of crash detection algorithms have been developed, but the coverage of these algorithms has been limited to few scenarios. Road scenes and situations need to be considered in order to expand the scope of a collision detection system to include a variety of collision modes. The proposed algorithm effectively handles the x, y, and z axes of the sensor, while considering time and suggests a method suitable for various real worlds. To reduce nuisance and false crash detection events, the algorithm discriminated between driving mode and parking mode. The performance of the suggested algorithm was evaluated under various scenarios, and it successfully discriminated between driving and parking modes, and it adjusted crash detection events depending on the real scenario. The proposed algorithm is expected to efficiently manage the space and lifespan of the storage device by allowing the vehicle's black box system to store only necessary crash event's videos.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.2942-2950
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• 2010

Car black box helps to investigate the cause of accident by recording time, position and videos as well as shock information. In addition, the car black box need a function to support safe driving for preventing accident. The representative driving support function is a lane departure warning. In order to implement the function, it is necessary to carry out the image processing to detect the lane first. The image processing algorithm requires computational burden to handle so much data and complicated structure of algorithm. This paper describes the efficient image processing algorithm with relatively low amount of computation for car black box embedded platform to detect lanes from the real-time lane image.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.1-11
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• 2001

This paper presents a learning-based approach for the construction of license Plate recognition system. The system consist of three modules. They are respectively, car detection module, license plate recognition module and recognition module. Car detection module detects a car in the given image sequence obtained from the camera with simple color-based approach. Segmentation module extracts the license plate in detect car image using neural network as filters for analyzing the color and texture properties of license plate. Recognition module then reads characters in detected license plate with support vector machine (SVM)-based characters recognizer. The system has been tested from parking lot and tollgate, etc. and have show the following performances on average: Car detect rate 100%, segmentation rate 97.5%, and character recognition rate about 97.2%. Overall system performances is 94.7% and processing time is one sec. Then our propose system does well using real world.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.27-39
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• 2018

Since the global positioning system (GPS) has been included in mobile devices (e.g., for car navigation, in smartphones, and in smart watches), the impact of personal GPS log data on daily life has been unprecedented. For example, such log data have been used to solve public problems, such as mass transit traffic patterns, finding optimum travelers' routes, and determining prospective business zones. However, a real-time analysis technique for GPS log data has been unattainable due to theoretical limitations. We introduced a machine learning model in order to resolve the limitation. In this paper presents a new, three-stage real-time prediction model for a person's daily route activity. In the first stage, a machine learning-based clustering algorithm is adopted for place detection. The training data set was a personal GPS tracking history. In the second stage, prediction of a new person's transient mode is studied. In the third stage, to represent the person's activity on those daily routes, inference rules are applied.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.11
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• pp.1008-1016
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• 2015

This paper proposes a method for detecting the front side of vehicles. The method can find the car side with a license plate even with complicated and cluttered backgrounds. A convolutional neural network (CNN) is used to solve the detection problem as a unified framework combining feature detection, classification, searching, and localization estimation and improve the reliability of the system with simplicity of usage. The proposed CNN structure avoids sliding window search to find the locations of vehicles and reduces the computing time to achieve real-time processing. Multiple responses of the network for vehicle position are further processed by a weighted clustering and probabilistic threshold decision method. Experiments using real images in parking lots show the reliability of the method.