• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.76-79
• /
• 2009

This paper investigates the performance of a vehicular rear-view camera through quantifying the image quality based on several objective criteria from the ISO (International Organization for Standardization). In addition, various experimental environments are defined considering the conditions under which a rear-view camera may need to operate. The process for evaluating the performance of a rear-view camera is composed of five objective criteria: noise test, resolution test, OECF (opto-electronic conversion function) test, color characterization test, and pincushion and barrel distortion tests. The proposed image quality quantification method then expresses the results of each test as a single value, allowing easy evaluation. In experiments, the performance evaluation results are analyzed and compared with those for a regular digital camera.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.855-856
• /
• 2008

In this paper, we studied the performance evaluation of a vehicular rear-view camera through quantifying the image quality based on several objective criteria from the ISO (International Organization for Standardization). In addition, various experimental environments are defined considering the conditions under which a rear-view camera may need to operate. The process for evaluating the performance of a rear-view camera is composed of five objective criteria: noise test, resolution test, OECF (opto-electronic conversion function) test, color characterization test, and pincushion and barrel distortion tests. The proposed image quality quantification method then expresses the results of each test as a single value, allowing easy evaluation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.1
• /
• pp.152-164
• /
• 2015

A rapid development of semiconductors, sensors and mobile network technologies has enable that the embedded device includes high sensitivity sensors, wireless communication modules and a video processing module for vehicular environment, and many researchers have been actively studying the smart car technology combined on the high performance embedded devices. The vehicle is increased as the development of society, and the risk of accidents is increasing gradually. Thus, the advanced driver assistance system providing the vehicular status and the surrounding environment of the vehicle to the driver using various sensor data is actively studied. In this paper, we design and implement the smart vehicular camera device providing the V2X communication and gathering environment information. And we studied the method to create the metadata from a received video data and sensor data using video analysis algorithm. In addition, we invent S-ROI, D-ROI methods that set a region of interest in a video frame to improve calculation performance. We performed the performance evaluation for two ROI methods. As the result, we confirmed the video processing speed that S-ROI is 3.0 times and D-ROI is 4.8 times better than a full frame analysis.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.8
• /
• pp.843-850
• /
• 2016

This paper introduces a novel modulation scheme for vehicular communication in taking advantage of existing LED lights available on a car. Our proposed 2-Phase Shift Keying (2-PSK) is a spatial modulation approach in which a pair of LED light sources in a car (either rear LEDs or front LEDs) is used as a transmitter. A typical camera (i.e. low frame rate at no greater than 30fps) that either a global shutter camera or a rolling shutter camera can be used as a receiver. The modulation scheme is a part of our Image Sensor Communication proposal submitted to IEEE 802.15.7r1 (TG7r1) recently. Also, a neural network approach is applied to improve the performance of LEDs detection and decoding under the noisy situation. Later, some analysis and experiment results are presented to indicate the performance of our system

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.7
• /
• pp.878-883
• /
• 2018

This paper proposes a method for transmitting instruction between vehicles in a moving situation using RC Car having camera. Information of preceding RC Car was transmitted by LED using Optical Camera Communication(OCC). Rear RC Car follows the preceding one by analyzing transmitted OCC data based on image processing. Through this procedure, the information reception ratio according to the distance change of two RC Cars is confirmed. Through experiments, we showed that our proposed scheme enables the possibility of vehicle platooning.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.42 no.1
• /
• pp.181-192
• /
• 2017

Recently, many researches are carried out for Advanced Driver Assistant Systems(ADAS). Especially, many studies are carried out to analyze the road situation using road images. In order to improve the performance of the road situation analysis, it is necessary to acquire images with appropriate exposure time. In this paper, we design and implement multi exposure smart vehicular camera which provides road traffic information to driver. Proposed device can acquire road traffic information by on-board camera and various sensors. And we propose an auto exposure control algorithm for the road environment to increase accuracy of image recognition. In addition, we also propose the switching ROI method that apply existing ROI techniques to overcome a limited computation power of embedded devices. We developed prototype of multi exposure smart vehicular camera and performed experiments to evaluate proposed auto exposure control algorithm and switching ROI method. The results show that the average accuracy of image recognition increased by 13.45%.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.1
• /
• pp.142-152
• /
• 2016

GPS is not able to be used for indoor positioning and currently most of techniques emerging to overcome the limit of GPS utilize private wireless networks. However, these methods require high costs for installation and maintenance, and they are inappropriate to be used in the place where precise positioning is needed as in indoor parking lots. This paper proposes a vehicular indoor positioning method based on QR-code recognition. The method gets an absolute coordinate through QR-code scanning, and obtain the location (an relative coordinate) of a black-box camera using the tilt and roll angle correction through affine transformation, scale transformation, and trigonometric function. Using these information of an absolute coordinate and an relative one, the precise position of a car is estimated. As a result, average error of 13.79cm is achieved and it corresponds to just 27.6% error rate in contrast to 50cm error of the recent technique based on wireless networks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.12
• /
• pp.5978-5999
• /
• 2018

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.1
• /
• pp.191-197
• /
• 2015

In this paper, we propose the system which recognize the gaze using Random Forests in vehicular environment based on smart-phone. Proposed system is mainly composed of the following: face detection using Adaboost, face component estimation using Histograms, and gaze recognition based on Random Forests. We detect a driver based on the image information with a smart-phone camera, and the face component of driver is estimated. Next, we extract the feature vectors from the estimated face component and recognize gaze direction using Random Forest recognition algorithm. Also, we collected gaze database including a variety gaze direction in real environments for the experiment. In the experiment result, the face detection rate and the gaze recognition rate showed 82.02% and 84.77% average accuracies, respectively.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.1
• /
• pp.428-435
• /
• 2017

In this paper, we propose a novel motion field estimation algorithm for which a U-disparity map and forward-and-backward error removal are applied in a vehicular environment. Generally, a motion exists in an image obtained by a camera attached to a vehicle by vehicle movement; however, the obtained motion vector is inaccurate because of the surrounding environmental factors such as the illumination changes and vehicles shaking. It is, therefore, difficult to extract an accurate motion vector, especially on the road surface, due to the similarity of the adjacent-pixel values; therefore, the proposed algorithm first removes the road surface region in the obtained image by using a U-disparity map, and uses then the optical flow that represents the motion vector of the object in the remaining part of the image. The algorithm also uses a forward-backward error-removal technique to improve the motion-vector accuracy and a vehicle's movement is predicted through the application of the RANSAC (RANdom SAmple Consensus) to the previously obtained motion vectors, resulting in the generation of a motion field. Through experiment results, we show that the performance of the proposed algorithm is superior to that of an existing algorithm.