• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.372-375
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• 2008

Drivers need to process dynamic stimulus in real - time with full attention from Telematics environment. However, as the information technology revolution brings more and more data into vehicles, all of it competing for the drivers' attention, the development of automated assistance for driver information processing becomes increasingly import ant. There for e, drivers' workload is very essential factor for safety driving in Telematics environment. In this paper, we have discussed driver distraction caused by driver conversation while driving and proposed voice activity detection algorithm for measuring driver workload. Finally, we show how voice activity detection system works for measuring driver workload.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.252-263
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• 2016

Road sign is an important street furniture that gives some information such as road conditions, driving direction and condition for a driver. Thus, road sign is a major target of image recognition for self-driving car, ADAS(autonomous vehicle and intelligent driver assistance systems), and ITS(intelligent transport systems). In this paper, an enhanced road sign recognition system is proposed for MMS(Mobile Mapping System) using the single camera and GPS. For the proposed system, first, a road sign recognition scheme is proposed. this scheme is composed of detection and classification step. In the detection step, object candidate regions are extracted in image frames using hybrid road sign detection scheme that is based on color and shape features of road signs. And, in the classification step, the area of candidate regions and road sign template are compared. Second, a Geo-marking scheme for geo-content that is consist of road sign image and coordinate value is proposed. If the serious situation such as car accident is happened, this scheme can protect geographical information of road sign against illegal users. By experiments with test video set, in the three parts that are road sign recognition, coordinate value estimation and geo-marking, it is confirmed that proposed schemes can be used for MMS in commercial area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1125-1132
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• 2012

The researches on driver assistance systems that can prevent an accident have been actively performed due to social issues of traffic accidents with development of vehicle industry in recent. It is required for researchers to develope systems which assist driver's perception and judgment when considering that over 70% of traffic accidents occur by drivers' carelessness and 75% of the total accidents occur at the speed of less 29km per hour. In this paper, we implemented a front and rear vehicle monitoring system that monitors distance from a vehicle to obstacles in real-time at the low-speed or back-ward driving. The proposed system consists of ultrasonic sensors of high angle and wide angle of beam spread, ATmega128, and DSP processor.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.591-598
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• 2016

The final step of driving a car is parking, which is the most difficult part for people learning to drive. Parking in narrow parking spaces is difficult for both ordinary drivers and beginners. To solve this problem, the development of SPAS (Smart Parking Assist System), ACC (Automatic Control System) improves the convenience of drivers. In addition, parking assistance systems have been developed to recognize more accurately the surrounding environment to the driver using the ultrasound, camera, thermal camera, and radar. This paper proposes the reverse turning radius to process images as if the camera is located in the center of the vehicle regardless of the actual camera position. In addition, it generates the parking guidelines through verification using the vehicle.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.274-286
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• 2022

Multi-object tracking has been studied for a long time under computer vision and plays a critical role in applications such as autonomous driving and driving assistance. Multi-object tracking techniques generally consist of a detector that detects objects and a tracker that tracks the detected objects. Various publicly available datasets allow us to train a detector model without much effort. However, there are relatively few publicly available datasets for training a tracker model, and configuring own tracker datasets takes a long time compared to configuring detector datasets. Hence, the detector is often developed separately with a tracker module. However, the separated tracker should be adjusted whenever the former detector model is changed. This study proposes a system that can train a model that performs detection and tracking simultaneously using only the detector training datasets. In particular, a Siam network with augmentation is used to compose the detector and tracker. Experiments are conducted on public datasets to verify that the proposed algorithm can formulate a real-time multi-object tracker comparable to the state-of-the-art tracker models.

• Journal of the Korean Society of Safety
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• v.33 no.4
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• pp.111-118
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• 2018

Korea is facing a surge in the aging population, showing that population aged 65 and above will be accounted for 42.5% of the total population in 2065 with the emphasis on the over-80 population consisting of 19.2%. In response to this abrupt change in population structure, the number of traffic fatality accident referring to older driver as aged 65+ years had been increasing from 605 fatalities in 2011 to 815 fatalities in 2015 resulting in increases in 34.7% in oppose to happening to decreases in 17.2% about non-older driver. With Logit analysis based on Newton-Raphson algorithm utilizing older driver's traffic fatality data for the 2011-2015 years, it was found that the likelihood of an accident resulting in a fatality for super older driver aged 80 years and above considerably increased compared to other older driver with aging classification: 2.24 times for violation of traffic lane, 2.04 times for violation of U-turn, 1.48 times for violation of safety distance, 1.35 times for violation of obstacle of passing; also average annual increase of traffic accident cost related to super older driver was fairly increased rather than other older driver groups. Hence, this study proposes that improving and amending transport safety system and Road Traffic Act for super older driver needs to be urgently in action about license management, safe driving education, etc. when considering the increase of over-80 population in the near future. Also, implementing a social agreement with all ages and social groups to apply with advanced driver assistance system for older driver groups will be able to become a critical factor to enhance safe driving over the face of the country.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.979-989
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• 2016

Traversable region detection is an essential step for advanced driver assistance systems and self-driving car systems, and it has been conducted by detecting lanes from input images. The performance can be unreliable, however, when the light condition is poor or there exist no lanes on the roads. To solve this problem, this paper proposes an algorithm which utilizes the information about the number of lanes and texture analysis. The proposed algorithm first specifies road region candidates by utilizing the number of lanes information. Among road region candidates, the road region is determined as the region in which texture is homogeneous and texture discontinuities occur around its boundaries. Traversable region is finally detected by dividing the estimated road region with the number of lanes information. This paper combines the proposed algorithm with a lane detection-based method to construct a system, and simulation results show that the system detects traversable region even on the road with poor light conditions or no lanes.

• Journal of Broadcast Engineering
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• v.17 no.6
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• pp.1069-1079
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• 2012

Lane detection with the use of a camera is an essential task required for the development of advanced driving assistance system. In this paper, edges of the lane are generated by applying Canny's method. The edge detection usually makes different results for several environmental conditions depending on the clearness of lane quality, so that it sometimes causes wrong lane detection. Therefore, we propose robust algorithm to environmental changes that automatically adjusts parameter for edge detection and generates edges more stably. Based on the acquired edges, we finally generate the spline curve of lane by using Catmull Rom spline.