• Journal of Information Processing Systems
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• 제1권1호
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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.

• 한국멀티미디어학회논문지
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• 제19권12호
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• pp.1909-1918
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• 2016

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

• 한국산학기술학회논문지
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• 제16권3호
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• pp.2192-2200
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• 2015

본 논문에서는 히스토그램 균등화 기반의 효율적인 차량용 영상 보정 알고리즘을 제안한다. 제안된 차량용 영상보정 알고리즘은 움직임 추정 및 움직임 보상을 통해 차량용 영상의 흔들림을 제거하였다. 그리고 영상을 보정하기 위해 영상을 일정 영역으로 분할하여 각각의 서브 영상에서 픽셀 값의 히스토그램을 계산하였다. 또한, 기울기를 조절하여 영상을 개선하였다. 제안된 알고리즘은 IP에 적용하여 성능 및 시간, 영상의 차이점을 평가하고, 차량용 카메라 영상의 흔들림 제거와 영상 개선을 확인하였다. 본 논문에서 제안된 차량용 영상 보정 알고리즘은 기존 차량 영상 안정화 기술과 비교하였을 때, 차량용 영상에 대한 흔들림 제거는 메모리를 사용하지 않고 실시간 처리를 했기 때문에 효율성을 입증하였다. 그리고 블록 정합을 통한 연산으로 계산 시간 감소 효과를 얻었고, 노이즈가 가장 적고 영상의 자연스러움이 더 뛰어난 복원 결과를 얻을 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2962-2964
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• 1999

We know the traffic information about the velocity and position of vehicle by extraction and tracking vehicle from continuosly obtained road image of camera. The conventional method of vehicle detection indicate increment of error due to headlight and taillight in night road image. This paper show such as vehicle detection of binary, Edge detection. amalgamation of image are applied to extract the vehicle, and Kalman filter is adaptive methods for tracking position and velocity of vehicle.

• 한국정밀공학회지
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• 제31권3호
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• pp.233-241
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• 2014

This paper contributes to development of a new method for detecting rear-side vehicles and estimating the positions for blind spot region or providing the lane change information by using vision systems. Because the real image acquired during car driving has a lot of information including the target vehicle and background image as well as the noises such as lighting and shading, it is hard to extract only the target vehicle against the background image with satisfied robustness. In this paper, the target vehicle has been detected by repetitive image processing such as sobel and morphological operations and a Kalman filter has been also designed to cancel the background image and prevent the misreading of the target image. The proposed method can get faster image processing and more robustness rather than the previous researches. Various experiments were performed on the highway driving situations to evaluate the performance of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1130-1133
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• 2003

The goal of this research is developing advanced lane detecting algorithm for unmanned vehicle. Previous lane detecting method to bring on error become of the lane loss and noise. Therefore, new algorithm developed to get exact information of lane. This algorithm can be used to AGV(Autonomous Guide Vehicle) and LSWS(Lane Departure Warning System), ACC(Adapted Cruise Control). We used 1/10 scale RC car to embody developed algorithm. A CCD camera is installed on top of vehicle. Images are transmitted to a main computer though wireless video transmitter. A main computer finds information of lane in road image. And it calculates control value of vehicle and transmit these to vehicle. This algorithm can detect in input image marked by 256 gray levels to get exact information of lane. To find the driving direction of vehicle, it search line equation by curve fitting of detected pixel. Finally, author used median filtering method to removal of noise and used characteristic part of road image for advanced of processing time.

• Journal of Information Processing Systems
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• 제19권5호
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• pp.663-672
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• 2023

Most vehicle detection methods have poor vehicle feature extraction performance at night, and their robustness is reduced; hence, this study proposes a night vehicle detection method based on style transfer image enhancement. First, a style transfer model is constructed using cycle generative adversarial networks (cycleGANs). The daytime data in the BDD100K dataset were converted into nighttime data to form a style dataset. The dataset was then divided using its labels. Finally, based on a YOLOv5s network, a nighttime vehicle image is detected for the reliable recognition of vehicle information in a complex environment. The experimental results of the proposed method based on the BDD100K dataset show that the transferred night vehicle images are clear and meet the requirements. The precision, recall, mAP@.5, and mAP@.5:.95 reached 0.696, 0.292, 0.761, and 0.454, respectively.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2012년도 제46차 하계학술발표논문집 20권2호
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• pp.433-434
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• 2012

본 연구에서는 쓰레기 매립장 내에서의 차량의 이동경로와 매립시점을 판단할 수 있는 방법을 제시한다. 현재의 영상과 배경영상의 차를 구하여 차량의 이동경로를 추적하고 이동경로의 형태로부터 정차 여부를 판단할 수 있으며, 정차시의 영상과 배출구의 개방영상과의 비교를 통해 폐기물의 매립시점을 검출할 수 있도록 한다.

• 반도체디스플레이기술학회지
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• 제13권2호
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• pp.7-12
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• 2014

A vehicle license plate includes the most important information for recognition and classification of the vehicle. In this paper, we propose a vehicle license plate recognition system using image binarization and template matching. In the proposed system, an image of the vehicle license plate is converted into a gray scale image and the gray image undergoes the binarization process. Finally, the numbers on the plate are extracted from the binary image using the template matching algorithm.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2003년도 추계학술발표회 논문집
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• pp.187-192
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• 2003

of the road would be a key index judged for a safety at the vehicle driving on the road. In Korea, as seen through a lot of documents, the vehicle driving speed is much faster compared with the design speed. The vehicle driving speed is an important element to get to know the vehicle driving characteristics. However, it is not easy to obtain the vehicle driving speed relating to vehicles' consecutive movements just merely through the presently used methods of vehicle driving speed. In consequence, this study has conducted photographing vehicle movements by use of digital moving images. Based on digital moving Images pictured, we have obtained a certain time interval frame and extracted out vehicles' coordinates and calculated vehicle speed from the firstly rectified image and the secondly rectified image. We could obtain comparatively exact results in the calculation of vehicle driving speed as errors of about 4%, as a result of comparison and verification of vehicle speed calculated from the digital moving images and the speed obtained from DGPS.