• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.5
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• pp.1119-1128
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• 2013

In this paper, we propose an algorithm to detect and track the human face with a GPU-based high speed. Basically the detection algorithm uses the existing Adaboost algorithm but the search area is dramatically reduced by detecting movement and skin color region. Differently from detection process, tracking algorithm uses only depth information. Basically it uses a template matching method such that it searches a matched block to the template. Also, In order to fast track the face, it was computed in parallel using GPU about the template matching. Experimental results show that the GPU speed when compared with the CPU has been increased to up to 49 times.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1066-1079
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• 2023

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.68-81
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• 2014

This study aims to apply and evaluate the automatic DAD analysis method, which is able to establish the depth-area relationship more efficiently and accurately for point-to-areal rainfall conversion. First, the proposed automatic DAD analysis method tracks the expansion route of area from the storm center, and it is divided into Box-tracking, Point-tracking, Advanced point-tracking according to tracking method. After applying the proposed methods to 10 events occurred in Yongdam-watershed area, we confirmed that the Advanced point-tracking method makes it possible to estimate the maximum average areal rainfal(MAAR) more accurately with consideration of the storm movement and the multi-centered storm. In addition, Advanced point-tracking could reduce the errors of the estimated MAAR induced by increasing the area because it can estimate MAAR for each storm center and compare them at the same time. Finally, the DAD curve for the study area could be derived based on the DAD analysis of the selected 10 events.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.101-102
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• 2011

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.61-67
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• 2017

Recently, CCTV has been combined with areas such as big data, artificial intelligence, and image analysis to detect various abnormal behaviors and to detect and analyze the overall situation of objects such as people. Image analysis research for this intelligent video surveillance function is progressing actively. However, CCTV images using 2D information generally have limitations such as object misrecognition due to lack of topological information. This problem can be solved by adding the depth information of the object created by using two cameras to the image. In this paper, we perform background modeling using Mixture of Gaussian technique and detect whether there are moving objects by segmenting the foreground from the modeled background. In order to perform the depth information-based segmentation using the RGB information-based segmentation results, stereo-based depth maps are generated using two cameras. Next, the RGB-based segmented region is set as a domain for extracting depth information, and depth-based segmentation is performed within the domain. In order to detect the center point of a robustly segmented object and to track the direction, the movement of the object is tracked by applying the CAMShift technique, which is the most basic object tracking method. From the experiments, we prove the efficiency of the proposed object detection and tracking method using the RGB-D model.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.113-121
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• 2020

Human Pose Estimation (HPE) which localizes the human body joints becomes a high potential for high-level applications in the field of computer vision. The main challenges of HPE in real-time are occlusion, illumination change and diversity of pose appearance. The single RGB image is fed into HPE framework in order to reduce the computation cost by using depth-independent device such as a common camera, webcam, or phone cam. However, HPE based on the single RGB is not able to solve the above challenges due to inherent characteristics of color or texture. On the other hand, depth information which is fed into HPE framework and detects the human body parts in 3D coordinates can be usefully used to solve the above challenges. However, the depth information-based HPE requires the depth-dependent device which has space constraint and is cost consuming. Especially, the result of depth information-based HPE is less reliable due to the requirement of pose initialization and less stabilization of frame tracking. Therefore, this paper proposes a new method of HPE which is robust in estimating self-occlusion. There are many human parts which can be occluded by other body parts. However, this paper focuses only on head self-occlusion. The new method is a combination of the RGB image-based HPE framework and the depth information-based HPE framework. We evaluated the performance of the proposed method by COCO Object Keypoint Similarity library. By taking an advantage of RGB image-based HPE method and depth information-based HPE method, our HPE method based on RGB-D achieved the mAP of 0.903 and mAR of 0.938. It proved that our method outperforms the RGB-based HPE and the depth-based HPE.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.62-67
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• 2009

Many interaction methods for augmented reality has attempted to reduce difficulties in tracking of interaction subjects by either allowing a limited set of three dimensional input or relying on auxiliary devices such as data gloves and paddles with fiducial markers. We propose Spatial Interaction (SPINT), a noncontact passive method that observes an occupancy state of the spaces around target virtual objects for interpreting user input. A depth-sensing camera is introduced for constructing the virtual space sensors, and then manipulating the augmented space for interaction. The proposed method does not require any wearable device for tracking user input, and allow versatile interaction types. The depth perception anomaly caused by an incorrect occlusion between real and virtual objects is also minimized for more precise interaction. The exhibits of dynamic contents such as Miniature AR System (MINARS) could benefit from this fluid 3D user interface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.378-390
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• 2015

Image projectors can turn any surface into a display. Integrating a surface projection with a user interface transforms it into an interactive display with many possible applications. Hand gesture interfaces are often used with projector-camera systems. Hand detection through color image processing is affected by the surrounding environment. The lack of illumination and color details greatly influences the detection process and drops the recognition success rate. In addition, there can be interference from the projection system itself due to image projection. In order to overcome these problems, a gesture interface based on depth images is proposed for projected surfaces. In this paper, a depth camera is used for hand recognition and for effectively extracting the area of the hand from the scene. A hand detection and finger tracking method based on depth images is proposed. Based on the proposed method, a touch interface for the projected surface is implemented and evaluated.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.264-269
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• 2002

In this paper, Η$_{\infty}$ depth and course controller of an AUV(Autonomous Underwater Vehicle) using Η$_{\infty}$ servo control is proposed. The Η$_{\infty}$ servo problem is formulated to design the controllers satisfying a robust tracking property with modeling errors and disturbances. The solution of the Η$_{\infty}$ servo problem is as fellows: first, this problem is modified as an Η$_{\infty}$ control problem for the generalized plant that includes a reference input mode, and then a sub-optimal solution that satisfies a given performance criteria is calculated by LMI(Linear Matrix Inequality) approach. The Η$_{\infty}$ depth and course controller are designed to satisfy with the robust stability about the modeling error generated from the perturbation of the hydrodynamic coefficients and the robust tracking property under disturbances(wave force, wave moment, tide). The performances of the designed controllers are evaluated with computer simulations, and finally these simulation results show the usefulness and application of the proposed Η$_{\infty}$ depth and course control system.