• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.55 no.6
• /
• pp.954-959
• /
• 2022

Marine outfalls are used to discharge treated liquid effluents to the environment. An efficiently designed, constructed and operated marine outfall effectively dilutes the discharged effluent, thereby reducing the risk to biota and humans dependent upon the marine environment. In this study, we investigated the effluent transport from a marine outfall at different depths in Masan Bay. A particle-tracking model was used to predict the dispersion of effluent. The model results indicate that some particles released from a depth of 13 m move to the inner area of Masan Bay within 48 h. As the release depth increases after 48 h, the particles move further southward. This suggests that effluent from the outer area of Masan Bay can affect the inner area, and that this effect can be reduced by increasing the depth of effluent release.

• Journal of Korea Water Resources Association
• /
• v.47 no.4
• /
• pp.357-370
• /
• 2014

This study aims to develop a rainfall field tracking method for depth-area-duration (DAD) analysis and assess whether the proposed tracking methods are able to properly estimate the maximum average areal rainfall (MAAR) within the study area during a rainfall period. We proposed three different rainfall field tracking algorithms (Box-tracking, Point-tracking, Advanced point-tracking) and then applied them to the virtual rainfall field with 1hr duration and also compared DAD curves of each method. In addition, we applied the three tracking methods and a traditional GIS-based tool to the typhoon 'Nari' rainfall event of the Yongdam-Dam watershed and then assess applicability of the proposed methods for DAD analysis. The results showed that Box-tracking was much faster than the other two tracking methods in terms of searching for the MAAR but it was impossible to describe rainfall spatial pattern during its tracking processes. On the other hand, both Point-tracking and Advanced point-tracking provided the MAAR by considering the spatial distribution of rainfall fields. In particular, Advanced point-tracking estimated the MAAR more accurately than Point-tracking in the virtual rainfall field, which has two rainfall centers with similar depths. The proposed automatic rainfall field tracking methods can be used as effective tools to analyze DAD relationship and also calculate areal reduction factor.

• Journal of KIISE:Software and Applications
• /
• v.34 no.10
• /
• pp.937-944
• /
• 2007

In this paper, we propose multiple human tracking with an moving stereo camera. The tracking process is based on mean shift algorithm which is using color information of the target. Color based tracking approach is invariant to translation and rotation of the target but, it has several problems. Because of mean shift uses color distribution, it is sensitive to color distribution of background and targets. In order to solve this problem, we combine color and depth information of target. Also, we build human body part model to handle occlusions and we have created adaptive box scale. As a result, the proposed method is simple and efficient to track multiple humans in real time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.30 no.2C
• /
• pp.16-24
• /
• 2005

In this paper, we proposed a face tracking algorithm for a viewpoint adaptive multi-view synthesis system. The original scene captured by a depth camera contains a texture image and 8 bit gray-scale depth map. From this original image, multi-view images can be synthesized which correspond to viewer's position by using geometrical transformation such as a rotation and a translation. The proposed face tracking technique gives a motion parallax cue by different viewpoints and view angles. In the proposed algorithm, tracking of viewer's dominant face initially established from camera by using statistical characteristics of face colors and deformable templates is done. As a result, we can provide motion parallax cue by detecting viewer's dominant face area and tracking it even under a heterogeneous background and can successfully display the synthesized sequences.

• Proceedings of the IEEK Conference
• /
• 2007.07a
• /
• pp.315-316
• /
• 2007

This paper presents a tracking algorithm, which is insensitive to light conditions. The proposed algorithm uses the depth information as well as the intensity information to track objects reliably. Specifically we use a disparity map to detect an object and employ the intensity histogram to track the motion of the object. Simulation results demonstrate the performance of the proposed algorithm.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.6
• /
• pp.1857-1862
• /
• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• Aerospace Engineering and Technology
• /
• v.11 no.2
• /
• pp.140-148
• /
• 2012

For autonomous navigation, map, pose tracking, and finding the shortest path are required. Because there is no GPS signal in indoor environment, the current position should be recognized in the 3D map by using image processing or something. In this paper, we explain 3D map creation technology by using depth camera like Kinect and pose tracking in 3D map by using 2D image taking from camera. In addition, the mechanism of avoiding obstacles is discussed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.189-192
• /
• 2009

We present a novel head tracking system for stereoscopic displays that ensures the viewer has a high degree of movement. The tracker is capable of segmenting the viewer from background objects using their relative distance. A depth camera is used to generate a key signal for head tracking application. A method of the moving parallax barrier is also introduced to supplement a disadvantage of the fixed parallax barrier that provides observation at the specific locations.

• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.8
• /
• pp.661-666
• /
• 2013

A state feedback controller with integration of output error is proposed for way-point tracking of an AUV (Autonomous Underwater Vehicle). For the steering control on the XY plane, the proposed controller uses three state variables (sway velocity, yaw rate, heading angle) and the integral of the steering error, and for the depth control on the XZ plane, it uses four state variables (pitch rate, depth, pitch angle) and the integral of the depth error. From the simulation using Matlab/Simulink, we verify that the performance of the proposed controller is satisfactory within an error range of 1m from the target way-point for arbitrarily chosen sets of consecutive way-points.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.3136-3150
• /
• 2015

A vision-based 3D tracking of articulated human hand is one of the major issues in the applications of human computer interactions and understanding the control of robot hand. This paper presents an improved approach for tracking and recovering the 3D position and orientation of a human hand using the Kinect sensor. The basic idea of the proposed method is to solve an optimization problem that minimizes the discrepancy in 3D shape between an actual hand observed by Kinect and a hypothesized 3D hand model. Since each of the 3D hand pose has 23 degrees of freedom, the hand articulation tracking needs computational excessive burden in minimizing the 3D shape discrepancy between an observed hand and a 3D hand model. For this, we first created a 3D hand model which represents the hand with 17 different parts. Secondly, Random Forest classifier was trained on the synthetic depth images generated by animating the developed 3D hand model, which was then used for Haar-like feature-based classification rather than performing per-pixel classification. Classification results were used for estimating the joint positions for the hand skeleton. Through the experiment, we were able to prove that the proposed method showed improvement rates in hand part recognition and a performance of 20-30 fps. The results confirmed its practical use in classifying hand area and successfully tracked and recovered the 3D hand pose in a real time fashion.