• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.302-307
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• 2019

In modern Anti-Submarine Warfare, there are various ways to locate a submarine in a two-dimensional space. For more effective tracking and attack against a submarine the depth of the target is a critical factor. However, it has been difficult to find out the depth of a submarine until now. In this paper a possible solution to the depth estimation of submarines is proposed utilizing DIFAR (Directional Frequency Analysis and Recording) sonobuoy information such as contact bearings at or prior to CPA (Closest Point of Approach) and the target's Doppler signals. The relative depth of the target is determined by applying the Pythagorean theorem to the slant range and horizontal range between the target and the hydrophone of a DIFAR sonobuoy. The slant range is calculated using the Doppler shift and the target's velocity. the horizontal range can be obtained by applying a simple trigonometric function for two consecutive contact bearings and the travel distance of the target. The simulation results show that the algorithm is subject to an elevation angle, which is determined by the relative depth and horizontal distance between the sonobuoy and target, and that a precise measurement of the Doppler shift is crucial.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1121-1141
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• 2020

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.51-60
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• 2007

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle(UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV's mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.165-169
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• 2006

Purpose of this research is estimation of water depth by hyperspectral remote sensing in area that access of ship is difficult This research used EO-1 Hyperion satellite imagery. Atmospheric and geometric correction is executed. Compress of band used MNF transforms. Diffuse Attenuation Coefficient of target area is decided in imagery for water depth estimation. Determination of Emdmember in pixel is using Linear Spectral Unmixing techniques. Water depth estimated using this result.

• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.216-222
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• 2008

Purpose of this research is estimation of water depth by hyperspectral remote sensing in area that access of ship is difficult. This research used EO-l Hyperion satellite imagery. Atmospheric and geometric correction is executed. Compress of band used MNF transforms. Diffuse Attenuation Coefficient of target area is decided in imagery for water depth estimation. Determination of Emdmember in pixel is using Linear Spectral Unmixing techniques. Water depth estimated using this result.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.210-217
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• 2022

Passive source depth estimation has been studied for decades since the source depth can be used for target classification, target tracking, etc. The purpose of this paper is to solve the problem of ambiguity in the previous paper [S.-il. Cho et al. (in Korean), J. Acoust. Soc. Kr. 38, 120-127 (2019)] that source depth is estimated in two points. The patterns of phase shift of Channel Impulse Response(CIR) reflected in ocean surface and bottom is used for removing ambiguity of the source depth estimation, and after removing ambiguity, source depth is estimated at one point through the intersection of CIR. In order to extract CIR in case of unknown source signal and continuous signal or noise, Ray-based blind deconvolution is used. The proposed algorithm is demonstrated through numerical simulation in ocean waveguide.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.659-666
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• 2021

With the continuous development of deep learning, human behavior recognition algorithms have achieved good results. However, in a multi-person recognition environment, the complex behavior environment poses a great challenge to the efficiency of recognition. To this end, this paper proposes a multi-person pose estimation model. First of all, the human detectors in the top-down framework mostly use the two-stage target detection model, which runs slow down. The single-stage YOLOv3 target detection model is used to effectively improve the running speed and the generalization of the model. Depth separable convolution, which further improves the speed of target detection and improves the model's ability to extract target proposed regions; Secondly, based on the feature pyramid network combined with context semantic information in the pose estimation model, the OHEM algorithm is used to solve difficult key point detection problems, and the accuracy of multi-person pose estimation is improved; Finally, the Euclidean distance is used to calculate the spatial distance between key points, to determine the similarity of postures in the frame, and to eliminate redundant postures.

• Journal of Korean Society on Water Environment
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• v.27 no.6
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• pp.822-829
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• 2011

The purpose of the present study is to estimate the ecological instream flow for conservation and restoration of fish habitat in running water ecosystem which has very important status for stream environment. Estimation of the ecological instream flow in the present study was carried out by application of a two-dimensional depth averaged model of river hydrodynamics, River2D model. It can model fish habitat in natural streams and rivers and assess the quality of physical habitat accoriding to the species preferences for habitat suitability. Zacco platypus and Zacco temmincki were selected as target fish species in the study area of the Seomjin river. The Habitat Suitability Criteria (HSC) developed by Sung et al. (2005) were used for target fish species, life stages and habitat conditions in the study. Weighted usable area (WUA) was computed by the River2D model considering preferences of target fish species for velocity, depth, and channel substrate. The result revealed that the ecological instream flow of $10.0m^3/s$ is needed to maintain the target fish habitat at each life stage in the river.

• Ocean Systems Engineering
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• v.11 no.4
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• pp.331-351
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• 2021

Underwater images are very much different from images taken on land, due to the presence of a higher disturbance ratio caused by the presence of water medium between the camera and the target object. These distortions and noises result in unclear details and reduced quality of the output image. An underwater image restoration method is proposed in this paper, which uses blurriness information, background light neutralization information, and red-light intensity to estimate depth. The transmission map is then estimated using the derived depth map, by considering separate attenuation coefficients for direct and backscattered signals. The estimated transmission map and estimated background light are then used to recover the scene radiance. Qualitative and quantitative analysis have been used to compare the performance of the proposed method against other state-of-the-art restoration methods. It has been shown that the proposed method can yield good quality restored underwater images. The proposed method has also been evaluated using different qualitative metrics, and results have shown that method is highly capable of restoring underwater images with different conditions. The results are significant and show the applicability of the proposed method for underwater image restoration work.

• Journal of Ocean Engineering and Technology
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• v.8 no.1
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• pp.62-70
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• 1994

In order to estimate the detection range of a active SONAR system, the SONAR equation is commonly used. In this paper, an algorithm to calculate detection range in active SONAR system as function of SONAR depth and target depth is presented. For given SONAR parameters and environment, the transmission loss and background level are found, signal excess is computed. Using log-normal distribution, signal excess is converted to detection probability at each range. Then, the detection range is obtained by integrating the detection probability as function of range for each depth. The proposed algorithm have been applied to the case of omni-directional source with center frequency 30Hz for summer and winter sound profiles. It is found that the optimal search depth is the source depth since the detection range increase at source depth where the signal excess is maximized.