• Journal of Auto-vehicle Safety Association
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• v.13 no.2
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• pp.35-41
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• 2021

In this study, improved path tracking control algorithm based on pure pursuit algorithm is newly proposed by using improved lane detection algorithm through real time post-processing with interpolation methodology. Since the original pure pursuit works well only at speeds below 20 km/h, the look-ahead distance is implemented as a sigmoid function to work well at an average speed of 45 km/h to improve tracking performance. In addition, a smoothing filter was added to reduce the steering angle vibration of the original algorithm, and the stability of the steering angle was improved. The post-processing algorithm presented has implemented more robust lane recognition system using real-time pre/post processing method with deep learning and estimated interpolation. Real time processing is more cost-effective than the method using lots of computing resources and building abundant datasets for improving the performance of deep learning networks. Therefore, this paper also presents improved lane detection performance by using the final results with naive computer vision codes and pre/post processing. Firstly, the pre-processing was newly designed for real-time processing and robust recognition performance of augmentation. Secondly, the post-processing was designed to detect lanes by receiving the segmentation results based on the estimated interpolation in consideration of the properties of the continuous lanes. Consequently, experimental results by utilizing driving guidance line information from processing parts show that the improved lane detection algorithm is effective to minimize the lateral offset error in the diverse maneuvering roads.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.9
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• pp.636-645
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• 1988

A new filter algorithm is suggested improving structurally the conventional extended Kalman filter of which the performance is dependent on the selection of the reference axes, by use of line-of-sight axes and gain rotation technique. The implementation method using microcomputer which implements tracking Kalman filter is introduced in terms of hardware and software. Then, through the simulation the performance of suggested filter is compared with that of conventional extended Kalman filter and the possibility of the real time tracking of moving target is investigated.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.357-360
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• 2007

An asset tracking using wireless sensor network is concerned with geographical locations of sensor nodes. The limited size of sensor nodes makes them attractable for tracking service, at the same time their size causes power restrictions, limited computation power, and storage restrictions. Due to such constrained capabilities, the wireless sensor network basically assumes the failure of sensor nodes. This causes a set of concerns in designing asset tracking system on wireless sensor network and one of the most critical factors is location uncertainty of sensor nodes. In this paper, we classify the location uncertainty problem in asset tracking system into following cases. First, sensor node isn't read at all because of sensor node failure, leading to misunderstanding that asset is not present. Second, incorrect location is read due to interference of RSSI, providing unreliable location of asset. We implemented and installed our asset tracking system in a real environment and continuously monitored the status of asset and measured error rate of location of sensor nodes. We present experimental results that demonstrate the location uncertainty problem in asset tracking system using wireless sensor network.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2594-2596
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• 2001

In this paper, a video tracker with a TMS320C31 DSP is designed and implemented. It is intended to work with PC through PCI Bus and can be used in real-time applications. The DSP board is capable of grabbing image data from camera, and calculating the position of a target, and trackig its movement. The tracking situation can be displayed in a monitor and displacement of the movement is fed back to pan and tilt the camera. Experimental results show that the tracker implemented here works well in real applications.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.746-751
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• 2006

In this paper, we present tracking algorithm for the indoor moving object. We research passive method using a camera and image processing. It had been researched to use dynamic based estimators, such as Kalman Filter, Extended Kalman Filter and Particle Filter for tracking moving object. These algorithm have a good performance on real-time tracking, but they have a limit. If the shape of object is changed or object is located on complex background, they will fail to track them. This problem will need the complicated image processing algorithm. Finally, a large algorithm is made from integration of dynamic based estimator and image processing algorithm. For eliminating this inefficiency problem, image based estimator, Mean-shift Algorithm is suggested. This algorithm is implemented by color histogram. In other words, it decide coordinate of object's center from using probability density of histogram in image. Although shape is changed, this is not disturbed by complex background and can track object. This paper shows the results in real camera system, and decides 3D coordinate using the data from mean-shift algorithm and relationship of real frame and camera frame.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.434-436
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• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.6 s.165
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• pp.903-911
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• 1999

A high speed tracking control system for 6-6 Stewart platform manipulator is designed for performing the model based joint-axis sliding mode control. Because of the complex dynamics and kinematics of the Stewart platform manipulator, two computer systems, consisting of a PC and a DSP, are adopted, so that real time tasks are run in synchronous and asynchronous modes. It is experimentally proven that the proposed control system makes the convenience in implementation of model based tracking control, so that it can achieve effective tracking control under relatively high speed and additional payload conditions.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.5
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• pp.76-84
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• 1994

Recently, Optical BJTC as a new approach for real-time multi-target tracking has been intensively studied. But the conventional system has some problems in the practical applications such as the false alarm and target missing and low correlation efficiency, and these poor performances are analyzed to be deeply dependent on the binarization method. So, in this paper, a new BJTC system which has the improved performances in target discrimination and diffraction efficiency is suggested, which is based on the JTPS having the same properties with those of the matched filter and new power spectrum binarization method to use effectively the high frequency components of the JTPS signal. Through the computer simulation and some experiments, the performances of the new BJTC tracking system are analyzed and proved to be superior to those of the conventional system baseds on Median method in multi- target tracking problems.

• Journal of Information Display
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• v.5 no.1
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• pp.41-46
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• 2004

In this paper, a new stereo 3D moving-target tracking system using the MSE (mean square error) and BPEJTC (binary phase extraction joint transform correlator) algorithms is proposed. A moving target is extracted from the sequential input stereo image by applying a region-based MSE algorithm following which, the location coordinates of a moving target in each frame are obtained through correlation between the extracted target image and the input stereo image by using the BPEJTC algorithm. Through several experiments performed with 20 frames of the stereo image pair with $640{\times}480$ pixels, we confirmed that the proposed system is capable of tracking a moving target at a relatively low error ratio of 1.29 % on average at real time.

• Journal of Sensor Science and Technology
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• v.18 no.3
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• pp.190-196
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• 2009

In a conventional camera system, a target tracking system consists of a camera part and a image processing part. However, in the field of the real time image processing, the vision chip for edge detection which was made by imitating the algorithm of humanis retina is superior to the conventional digital image processing systems because the human retina uses the parallel information processing method. In this paper, we present a high speed target tracking system using the function of the CMOS vision chip for edge detection.