• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1496-1504
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• 2004

In this paper, we propose an algorithm for initializing a target vehicle detection, tracking the vehicle and estimating the distance from it on the stereo images acquired from a forward-looking stereo camera mounted on a road driving vehicle. The process of vehicle detection extracts road region using lane recognition and searches vehicle feature from road region. The distance of tracking vehicle is estimated by TSS correlogram matching from stereo Images. Through the simulation, this paper shows that the proposed method segments, matches and tracks vehicles robustly from image sequences obtained by moving stereo camera.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.3
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• pp.258-266
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• 2014

We present a system for the real-time visual relative navigation of a fixed-wing unmanned aerial vehicle in a GPS-denied environment. An extended Kalman filter is used to construct a vision-aided navigation system by fusing the image processing results with barometer and inertial sensor measurements. Using a mean-shift object tracking algorithm, an onboard vision system provides pixel measurements to the navigation filter. The filter is slightly modified to deal with delayed measurements from the vision system. The image processing algorithm and the navigation filter are verified by flight tests. The results show that the proposed aerial system is able to maintain circling around a target without using GPS data.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.764-781
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• 2013

In this paper, we propose a highly precise vehicle detection method with low false alarm using billboard sweep stereo matching and multi-stage hypothesis generation. First, we capture stereo images from cameras established in front of the vehicle and obtain the disparity map in which the regions of ground plane or background are removed using billboard sweep stereo matching algorithm. And then, we perform the vehicle detection and tracking on the labeled disparity map. The vehicle detection and tracking consists of three steps. In the learning step, the SVM(support vector machine) classifier is obtained using the features extracted from the gabor filter. The second step is the vehicle detection which performs the sobel edge detection in the image of the left camera and extracts candidates of the vehicle using edge image and billboard sweep stereo disparity map. The final step is the vehicle tracking using template matching in the next frame. Removal process of the tracking regions improves the system performance in the candidate region of the vehicle on the succeeding frames.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.95-101
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• 2014

This paper explains effective tracking method for a vehicle cutting in a line on the road automatically. The method employs KLT based on optical flow using play-back image sequence. Main contribution of this paper is play-back image sequence that is in order image frames for rewind direction from a reference point in time. The moment when recognizing camera can read a license plate very well can usually be the reference point in time. The biggest images of object traced can usually be obtained at this moment also. When optic flow is applied, the bigger image of the object traced can be obtained, the more feature points can be obtained. More many feature points bring good result of tracking object. After the recognizing cameras read a license plate on the vehicle suspected of cut-in-line violation, and then the system extracts the play-back image sequence from the tracking cameras for watching wide range. This paper compares using play-back image sequence as normal method for tracking to using play-forward image sequence as suggested method on the results of the experiment and also shows the suggested algorithm has a good performance that can be applied to the unmanned system for watching cut-in-line violation.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.92-101
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• 1996

Due to I:he rapid increase of vehicles and poor availability of roads, traffic congestion problem is about to explode. To solve this problem, we need real time information about traffic flow to control traffic signals dynamically. Until now loop coil is the most prevalent sensor used for obtaining traffic flow information. However, it is not able to track individual vehicles which is essential in estimating the average vehicle speed. As a result, image sensors started to find their role in this problem domain. Several systems based on image sensors were proposed which assumes either gray level or color image sequence. In this paper, we propose moving vehicle tracking method based on fizzy clustering assuming a wlor image sequenc.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.07a
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• pp.135-136
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• 2017

When driving at night time environment, the whole body of transports does not visible to us. Due to lack of light conditions, there are only two options, which is clearly visible their taillights and break lights. To improve the recognition correctness of vehicle detection, we present an approach to vehicle detection and tracking using finding contour of the object on binary image at night time. Bilateral filtering is used to make more clearly on threshold part. To remove unexpected small noises used morphological opening. In verification stage, paired tail lights are tracked during their existence in the ROI. The accuracy of the test results for vehicle detection is about 93%.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.369-377
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• 2005

This paper describes the realization of a coordinates tracking algorithm for an EOTS (Electro-Optical Tracking System). The EOTS stabilizes the image sensors, tracks targets automatically, and provides navigation capability for vehicles. The coordinates tracking algorithm calculates the azimuth and the elevation angle of an EOTS using the inertial navigation system and the attitude sensors of the vehicle, so that LOS designates the target coordinates which are generated by a Radar. In the error analysis, the unexpected behaviors of an EOTS due to the time delay and deadbeat of the digital signals of the vehicle equipments are anticipated and the countermeasures are suggested. The application of this algorithm to an EOTS will improve the operational capability by reducing the time which is required to find the target and support flight especially in the night time flight and the poor weather condition.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.615-618
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• 1999

In this paper, a object detection and tracking algorithm is presented which exhibits robust properties for image sequences with complex background. The proposed algorithm is composed of three parts: moving object detection, object tracking, and motion analysis. The moving object detection algorithm is implemented using a temporal median background method which is suitable for real-time applications. In the motion analysis, we propose a new technique for removing a temporal clutter, such as a swaying plant or a light reflection of a background object. In addition, we design a multiple vehicle tracking system based on Kalman filtering. Computer simulation of the proposed scheme shows its robustness for MPEG-7 test image sequences.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.355-358
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• 2006

An underwater mooing target position tracking system using image tracking system of CPMC is developed to use in a test basin. Generally the performance tests of Autonomous Underwater Vehicles(AUVs) are conducted in the sea. Some efforts to perform the test in a test basin are exist, because the real sea tests need much time and manpower. And also the real sea tests are high cost. There is a restriction to acquire the position of AUVs using sonar sensor system in the test tank, because many sound reflecters are exist in a test basin. In this paper a position tracking system for underwater mooing target developed to break though this restriction. A Tank-test is conducted to examine the performance of the position tracking system.