• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.380-392
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• 2013

A method to classify a golf swing motion into 7 sections using a Kinect sensor and a fuzzy system is proposed. The inputs to the fuzzy logic are the positions of golf club and its head, which are extracted from the information of golfer's joint position and color information obtained by a Kinect sensor. The proposed method consists of three modules: one for extracting the joint's information, another for detecting and tracking of a golf club, and the other for classifying golf swing motions. The first module extracts the hand's position among the joint information provided by a Kinect sensor. The second module detects the golf club as well as its head with the Hough line transform based on the hand's coordinate. Using a fuzzy logic as a classification engine reduces recognition errors and, consequently, improves the performance of robust classification. From the experiments of real-time video clips, the proposed method shows the reliability of classification by 85.2%.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.575-583
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• 2017

This paper describes the design and evaluation of a driving mode decision algorithm for automated driving for merge situations on highways. For the development of a highly automated driving control algorithm for merge situations, the driving mode decision is crucial for merging appropriately. There are two driving modes: lane keeping and lane changing (merging). The merge mode decision is determined based on the state of the surrounding vehicles and the remaining length of the merge lane. In the merge mode decision algorithm, merge possibility and the desired merge position are decided to change the lane safely and quickly. A safety driving envelope is defined based on the desired driving mode using the information on the surrounding vehicles' behaviors. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope, a motion planning controller is designed using model predictive control (MPC), with constraints that are decided considering the vehicle dynamics, safe driving envelope, and actuator limit. The proposed control algorithm has been evaluated via computer simulation studies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.72-82
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• 2004

This paper proposes more robust error concealment techniques (ECTs) for MPEG-2 intra coded frame. MPEG-2 source coding algorithm is very sensitive to transmission errors due to the use of variable-length coding. The transmission errors are corrected by error correction scheme, however, they cannot be revised properly. Error concealment (EC) is used to conceal the errors which are not corrected by error correction and to provide minimum visual distortion at the decoder. If errors are generated in intra coded frame, that is the starting frame of GOP, they are propagated to other inter coded frames due to the nature of motion compensated prediction coding. Such propagation of error may cause severe visual distortion. The proposed algorithm in this paper utilizes the spatio-temporal information of neighboring inter coded frames to conceal the successive slices errors occurred in I-frame. The proposed method also overcomes the problems that previous ECTs reside. The proposed algorithm generates consistent performance even in network where the violent transmission errors frequently occur. Algorithm is performed in MPEG-2 video codec and we can confirm that the proposed algorithm provides less visible distortion and higher PSNR than other approaches through simulations.

• Journal of Korea Multimedia Society
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• v.15 no.8
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• pp.963-976
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• 2012

'Gesture' except for voice is the most intuitive means of communication. Thus, many researches on how to control computer using gesture are in progress. User detection and tracking in these studies is one of the most important processes. Conventional 2D object detection and tracking methods are sensitive to changes in the environment or lights, and a mix of 2D and 3D information methods has the disadvantage of a lot of computational complexity. In addition, using conventional 3D information methods can not segment similar depth object. In this paper, we propose object detection and tracking method using Depth Projection Map that is the cumulative value of the depth and motion information. Simulation results show that our method is robust to changes in lighting or environment, and has faster operation speed, and can work well for detection and tracking of similar depth objects.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• Journal of Advanced Navigation Technology
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• v.21 no.6
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• pp.554-560
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• 2017

Accurate altimetry is required for the reliable flight control of drones or unmanned air vehicles (UAVs), and the radar altimeter is commonly used owing to its accuracy for the ground level. Due to the limitation for size, weight and power consumption, the frequency modulated continuous wave (FMCW) radar is appropriate for drone because it has lower complexity than that of pulse Doppler (PD) radar. Especially, fast-ramp FMCW radar, which transmits linear FM signal during very short period, is generally utilized, because it is robust for the ego-motion of drone. Therefore, we present the design and implementation results of the radar signal processor (RSP) for fast-ramp FMCW radar system. The proposed RSP was designed with Verilog-HDL and implemented with Altera Cyclone-IV FPGA device. Implementation results show that the proposed RSP includes 27,523 logic elements, 15,798 registers and memory of 138Kbits and can measure the altimeter at the rate of 100Hz with the operating frequency of 50MHz.