• The Journal of Korea Robotics Society
• /
• v.18 no.1
• /
• pp.59-65
• /
• 2023

This paper proposes an Extended and Adaptive Inverse Perspective Mapping (EA-IPM) model that can obtain an accurate bird's-eye view (BEV) from the forward-looking monocular camera on the sidewalk with various curves. While Inverse Perspective Mapping (IPM) is a good way to obtain ground information, conventional methods assume a fixed relationship between the camera and the ground. Due to the nature of the driving environment of the mobile robot, there are more walking environments with frequent motion changes than flat roads, which have a fatal effect on IPM results. Therefore, we have developed an extended IPM process to be applicable in IPM on sidewalks by adding a formula for complementary Y-derive processes and roll motions to the existing adaptive IPM model that is robust to pitch motions. To convince the performance of the proposed method, we evaluated our results on both synthetic and real road and sidewalk datasets.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.8
• /
• pp.1666-1671
• /
• 2002

A lane sensing algorithm using vision sensors is developed based on lane geometry models. The parameters of the lane geometry models are estimated by a Kalman filter and utilized to reconstruct the lane geometry in the global coordinate. The inverse perspective mapping from image plane to global coordinate assumes earth to be flat, but roll and pitch motions of a vehicle are considered from the perspective of the lane sensing. The proposed algorithm shows robust lane sensing performance compared to the conventional algorithms.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.13 no.4
• /
• pp.195-204
• /
• 2018

Lane keeping assist and departure prevention system are the key functions of ADAS. In this paper, we propose lane detection method which uses Gaussian function based RANSAC. The proposed method consists mainly of IPM (inverse perspective mapping), Canny edge detector, and Gaussian function based RANSAC (Random Sample Consensus). The RANSAC uses Gaussian function to extract the parameters of straight or curved lane. The proposed RANSAC is different from the conventional one, in the following two aspects. One is the selection of sample with different probability depending on the distance between sample and camera. Another is the inlier sample score that assigns higher weights to samples near to camera. Through simulations, we show that the proposed method can achieve good performance in various of environments.

• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.23-26
• /
• 2000

An improved inverse perspective mapping (IIPM) is proposed so as to reduce computational expense of recovery of 3D road surface. An experimental system based on IIPM is developed to detect lane parameters for a driver assistant system. A re-organized image is obtained quickly and exactly by IIPM. Efficient preprocessing techniques are used to enhance the information of lane and obstacles. Lane in the preprocessed. image is located with region identification. Lane parameters are estimated effectively. An algorithm to adaptively modify the parameters of IIPM is given. Properties of obstacle on 3D road surface are discussed and used to detect obstacles in the current lane and neighboring lanes. Experimental results show that the new method can extract lane state information effectively.

• Journal of Broadcast Engineering
• /
• v.22 no.4
• /
• pp.448-461
• /
• 2017

This paper proposes a vehicle counting and speed estimation method for traffic situation analysis in road CCTV videos. The proposed method removes a distortion in the images using Inverse perspective Mapping, and obtains specific region for vehicle counting and speed estimation using lane detection algorithm. Then, we can obtain vehicle counting and speed estimation results from using optical flow at specific region. The proposed method achieves stable accuracy of 88.94% from several CCTV images by regional groups and it totally applied at 106,993 frames, about 3 hours video.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.4
• /
• pp.201-208
• /
• 2015

This paper proposes a method for detecting the direction indicator shown in the road surface efficiently from the black box system installed on the vehicle. In the proposed method, the direction indicators are detected by inverse perspective mapping(IPM) and bag of visual features(BOF)-based NN classifier. In order to apply the proposed method to real-time environments, the candidated regions of direction indicator in an image only performs IPM, and BOF-based NN is used for the classification of feature information from direction indicators. The results of applying the proposed method to the road surface direction indicators detection and recognition, the detection accuracy was presented at least about 89%, and the method presents a relatively high detection rate in the various road conditions. Thus it can be seen that the proposed method is applied to safe driving support systems available.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.28 no.3
• /
• pp.230-237
• /
• 2004

Lane Sensing techniques based on vision sensors are regarded promising because they require little infrastructure on the highway except clear lane markers. However, they require more intelligent processing algorithms in vehicles to generate the previewed roadway from the vision images. In this paper, a lane sensing algorithm using vision sensors is developed to improve the sensing robustness. The parallel stereo-camera is utilized to regenerate the 3-dimensional road geometry. The lane geometry models are derived such that their parameters represent the road curvature, lateral offset and heading angle, respectively. The parameters of the lane geometry models are estimated by the Kalman filter and utilized to reconstruct the lane geometry in the global coordinate. The inverse perspective mapping from the image plane to the global coordinate considers roll and pitch motions of a vehicle so that the mapping error is minimized during acceleration, braking or steering. The proposed sensing system has been built and implemented on a 1/10-scale model car.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.103-107
• /
• 2013

In this paper, A real-time lane tracking algorithm is proposed for lane departure warning system. To eliminate perspective effect, input image is converted into Bird's View by inverse perspective mapping. Next, suitable features are extracted for lane detection. Lane feature that correspond to area of interest and RANSAC are used to detect lane candidates. And driving lane is decided by clustering of lane candidates. Finally, detected lane is tracked using the Kalman filter. Experimental results show that the proposed algorithm can be processed within 30ms and its detection rate is approximately 90% on the highway in a variety of environments such as day and night.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.13 no.4
• /
• pp.97-104
• /
• 2005

Vision-based lane sensing systems require accurate and robust sensing performance in lane detection. Besides, there exists trade-off between the computational burden and processor cost, which should be considered for implementing the systems in passenger cars. In this paper, a stereo vision-based lane detection system is developed with considering sensor configuration aspects. An inverse perspective mapping method is formulated based on the relative correspondence between the left and right cameras so that the 3-dimensional road geometry can be reconstructed in a robust manner. A new monitoring model for estimating the road geometry parameters is constructed to reduce the number of the measured signals. The selection of the sensor configuration and specifications is investigated by utilizing the characteristics of standard highways. Based on the sensor configurations, it is shown that appropriate sensing region on the camera image coordinate can be determined. The proposed system is implemented on a passenger car and verified experimentally.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.11
• /
• pp.2492-2498
• /
• 2013

In this paper, A lane tracking algoritm is proposed for lane departure warning system. To eliminate perspective effect, input image is converted into Bird's View by inverse perspective mapping. Next, suitable features are extracted for lane detection. Using clustering and lane similarity function with noise suppression features are extracted. Finally, lane model is calculated using RANSAC and lane model is tracked using Kalman Filter. Experimental results show that the proposed algorithm can be processed within 20ms and its detection rate approximately 90% on the highway in a variety of environments.