• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.6
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• pp.134-140
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• 2008

Preceding vehicle detection is a crucial issue for driver assistance system as well as for autonomous vehicle guidance function and it has to be performed with high reliability to avoid any potential collision. The vision-based preceded vehicle detection systems are regarded promising for this purpose because they require little infrastructure on a highway. However, the feasibility of these systems in passenger car requires accurate and robust sensing performance. In this paper, an preceded vehicle detection system is developed using stereo vision sensors. This system utilizes feature matching, epipoplar constraint and feature aggregation in order to robustly detect the initial corresponding pairs. After the initial detection, the system executes the tracking algorithm for the preceded vehicles including a leading vehicle. Then, the position parameters of the preceded vehicles or leading vehicles can be obtained. The proposed preceded vehicle detection system is implemented on a passenger car and its performances is verified experimentally.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.5
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• pp.183-192
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• 2003

Obstacle detection and avoidance are considered as one of the key technologies on an unmanned vehicle system. In this paper, we propose a method of obstacle detection and avoidance and it is composed of vehicle control, modeling, and sensor experiments. Obstacle detection and avoidance consist of two parts: one is longitudinal control system for acceleration and deceleration and the other is lateral control system for steering control. Each system is used for unmanned vehicle control, which notes its location, recognizes obstacles surrounding it, and makes a decision how fast to proceed according to circumstances. During the operation, the control system of the vehicle can detect obstacles and perform obstacle avoidance on the road, which involves vehicle velocity. In this paper, we propose a method for vehicle control, modeling, and obstacle avoidance, which are evaluated through road tests.

• Journal of Multimedia Information System
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• v.4 no.1
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• pp.9-18
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• 2017

Ubiquitous computing is a novel paradigm that is rapidly gaining in the scenario of wireless communications and telecommunications for realizing smart world. As rapid development of sensor technology, smart sensor system becomes more popular in automobile or vehicle. In this study, a new vehicle detection mechanism in real-time for blind spot area is proposed based on imaging sensors. To determine the position of other vehicles on the road is important for operation of driver assistance systems (DASs) to increase driving safety. As the result, blind spot detection of vehicles is addressed using an automobile detection algorithm for blind spots. The proposed vehicle verification utilizes the height and angle of a rear-looking vehicle mounted camera. Candidate vehicle information is extracted using adaptive shadow detection based on brightness values of an image of a vehicle area. The vehicle is verified using a training set with Haar-like features of candidate vehicles. Using these processes, moving vehicles can be detected in blind spots. The detection ratio of true vehicles was 91.1% in blind spots based on various experimental results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.129-136
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• 2012

In the US, Japan and Germany the Aerial-Based Vehicle Detection System, which collects real-time traffic data using the Unmanned Aerial Vehicle (UAV), helicopters or fixed-wing aircraft has been developed for the last several years. Therefore, this study was done to find out whether the Aerial-Based Vehicle Detection System could be used for real-time traffic data collection. For this purpose the study was divided into two parts. In the first part the possibility of retrieving real-time traffic data such as travel speed from the aerial photographic image using the image processing technique was examined. In the second part the quality of the retrieved real-time traffic data was examined to find out whether the data are good enough to be used as traffic information source. Based on the results of examinations we could conclude that it would not be easy for the Aerial- Based Vehicle Detection System to replace the present Vehicle Detection System due to technological difficulties and high cost. However, the system could be effectively used to make the emergency traffic management plan in case of incidents such as abrupt heavy rain, heavy snow, multiple pile-up, etc.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.719-724
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• 2012

A traffic detection system is a device that collects traffic information around an intersection. Most existing traffic detection systems provide very limited traffic information for signal control due to the restriction of vehicle detection area. A signal control scheme determines the transition among signal phases and the time that a phase lasts for. However, the existing signal control scheme do not resolve the traffic congestion effectively since they use restricted traffic information. In this paper, a new traffic detection system with a zone division signal control scheme is proposed to provide correct and detail traffic information and decrease the vehicle's waiting time at the intersection. The traffic detection system obtains traffic information in a way of vehicle-to-roadside communication between vehicles and sensor network. A new signal control scheme is built to exploit the sufficient traffic information provided by the proposed traffic detection system efficiently. Simulation results show that the proposed signal control scheme has 121 % and 56 % lower waiting time and delay time of vehicles at an intersection than other fuzzy signal control scheme.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.27-31
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• 2005

Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1092-1099
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• 2017

This paper proposes a novel way of vehicle detection and classification based on image features. There are two main processes in the proposed system, which are database construction and vehicle classification processes. In the database construction, there is a tight censorship for choosing appropriate images of the training set under the rigorous standard. These images are trained using Haar features for vehicle detection and histogram of oriented gradients extraction for vehicle classification based on the support vector machine. Additionally, in the vehicle detection and classification processes, the region of interest is reset using a number plate to reduce complexity. In the experimental results, the proposed system had the accuracy of 0.9776 and the $F_1$ score of 0.9327 for vehicle classification.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.4
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• pp.163-172
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• 2012

A driver assistance system is critical to improve a convenience and stability of vehicle driving. Several systems have been already commercialized such as adaptive cruise control system and forward collision warning system. Efficient vehicle detection is very important to improve such driver assistance systems. Most existing vehicle detection systems are based on a radar system, which measures distance between a host and leading (or oncoming) vehicles under various weather conditions. However, it requires high deployment cost and complexity overload when there are many vehicles. A camera based vehicle detection technique is also good alternative method because of low cost and simple implementation. In general, night time vehicle detection is more complicated than day time vehicle detection, because it is much more difficult to distinguish the vehicle's features such as outline and color under the dim environment. This paper proposes a method to detect vehicles at night time using analysis of a captured color space with reduction of reflection and other light sources in images. Four colors spaces, namely RGB, YCbCr, normalized RGB and Ruta-RGB, are compared each other and evaluated. A suboptimal threshold value is determined by Otsu algorithm and applied to extract candidates of taillights of leading vehicles. Statistical features such as mean, variance, skewness, kurtosis, and entropy are extracted from the candidate regions and used as feature vector for SVM(Support Vector Machine) classifier. According to our simulation results, the proposed statistical feature based SVM provides relatively high performances of leading vehicle detection with various distances in variable nighttime environments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.85-94
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• 2021

The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.390-395
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• 2009

In this paper, we design an on-road vehicle detection system based on the combination of a camera and a LIDAR system. In the proposed system, the candidate area is selected from the LIDAR data using a grouping algorithm. Then, the selected candidate area is scanned by an SVM to find an actual vehicle. The morphological edged images are used as features in a camera. The principal components of the edged images called eigencar are employed to train the SVM. We conducted experiments to show that the on-road vehicle detection system developed in this paper demonstrates about 80% accuracy and runs with 20 scans per second on LIDAR and 10 frames per second on camera.