• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.249-254
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• 2007

A mobile robot with various types of sensors and wireless camera is introduced. We show this mobile robot can detect objects well by combining the results of active sensors and image processing algorithm. First, to detect objects, active sensors such as infrared rays sensors and supersonic waves sensors are employed together and calculates the distance in real time between the object and the robot using sensor's output. The difference between the measured value and calculated value is less than 5%. We focus on how to detect a object region well using image processing algorithm because it gives robots the ability of working for human. This paper suggests effective visual detecting system for moving objects with specified color and motion information. The proposed method includes the object extraction and definition process which uses color transformation and AWUPC computation to decide the existence of moving object. Shape information and signature algorithm are used to segment the objects from background regardless of shape changes. We add weighing values to each results from sensors and the camera. Final results are combined to only one value which represents the probability of an object in the limited distance. Sensor fusion technique improves the detection rate at least 7% higher than the technique using individual sensor.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.29-35
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• 2023

CCTV is used for various purposes such as crime prevention, public safety reinforcement, and traffic management. However, as the range and resolution of the camera improve, there is a risk of exposing personal information in the video. Therefore, there is a need for new technologies that can identify individuals while protecting personal information in images. In this paper, we propose histogram-based singular value decomposition for object identification and tracking. The proposed method distinguishes different objects present in the image using color information of the object. For object recognition, YOLO and DeepSORT are used to detect and extract people present in the image. Color values are extracted with a black-and-white histogram using location information of the detected person. Singular value decomposition is used to extract and use only meaningful information among the extracted color values. When using singular value decomposition, the accuracy of object color extraction is increased by using the average of the upper singular value in the result. Color information extracted using singular value decomposition is compared with colors present in other images, and the same person present in different images is detected. Euclidean distance is used for color information comparison, and Top-N is used for accuracy evaluation. As a result of the evaluation, when detecting the same person using a black-and-white histogram and singular value decomposition, it recorded a maximum of 100% to a minimum of 74%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.1
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• pp.72-79
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• 2013

In wireless sensor networks, the selective wakeup scheme is one of the energy saving mechanisms, that is used for an object detecting or tracking. Recently, many protocols are proposed using the selective wakeup scheme for the continuous objects tracking such as forest fires and poison gas. They predict the future shape of continuous objects and activate only sensors in the predicted boundary area of the objects. It works correctly in a uniformly deployed wireless sensor networks. However, it cannot be directly applied to a randomly deployed sensor networks with voids. When the predicted area is in the void area, the activation message cannot reach and the predicted area cannot be activated at the right time. It leads to many detection errors for continuous object. Moreover, if a sensor is once foiled in a activation control then the next activation control might be continuously failed. The detection errors can be result in serious harm to people. In this paper, we propose a chaining selective wakeup scheme for robust continuous object tracking in wireless sensor networks. In our protocol, we collect the information of a void area during the network configuration time; if the next boundary area is in the void area, we activate the chained area surrounding the void area with activation control message.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.119-129
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• 2011

Vision-based Human computer interaction is an emerging field of science and industry to provide natural way to communicate with human and computer. Especially, recent increasing demands for mobile augmented reality require the development of efficient interactive technologies between the augmented virtual object and users. This paper presents a novel approach to construct marker-less mobile augmented reality object and control the object. Replacing a traditional market, the human hand interface is used for marker-less mobile augmented reality system. In order to implement the marker-less mobile augmented system in the limited resources of mobile device compared with the desktop environments, we proposed a method to extract an optimal hand region which plays a role of the marker and augment object in a realtime fashion by using the camera attached on mobile device. The optimal hand region detection can be composed of detecting hand region with YCbCr skin color model and extracting the optimal rectangle region with Rotating Calipers Algorithm. The extracted optimal rectangle region takes a role of traditional marker. The proposed method resolved the problem of missing the track of fingertips when the hand is rotated or occluded in the hand marker system. From the experiment, we can prove that the proposed framework can effectively construct and control the augmented virtual object in the mobile environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6089-6097
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• 2012

This study suggested a method of detecting an incident inside tunnel by using camera that is installed within the tunnel. As for the proposed incident detection method, a static object, travel except vehicles, smoke, and contra-flow were detected by extracting the moving object through using the real-time background image differencing after receiving image from the camera, which is installed inside the tunnel. To detect the moving object within the tunnel, the positive background image was created by using the moving information of the object. The incident detection method was developed, which is strong in a change of lighting that occurs within the tunnel, and in influence of the external lighting that occurs in the entrance and exit of the tunnel. To examine the efficiency of the suggested method, the experimental images were acquired from Marae tunnel and Expo tunnel in Yeosu of Jeonnam and from Unam tunnel in Imsil of Jeonbuk. Number of images, which were used in experiment, included 20 cases for static object, 20 cases for travel except vehicles, 4 cases for smoke, and 10 cases for contra-flow. As for the detection rate, all of the static object, the travel except vehicles, and the contra-flow were detected in the experimental image. In case of smoke, 3 cases were detected. Thus, excellent performance could be confirmed. The proposed method is now under operation in Marae tunnel and Expo tunnel in Yeosu of Jeonnam and in Unam tunnel in Imsil of Jeonbuk. To examine accurate efficiency, the evaluation of performance is considered to be likely to be needed after acquiring the incident videos, which actually occur within tunnel.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.211-217
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• 2012

Conventional GrabCut algorithm is semi-automatic algorithm that user must be set rectangle window surrounds the object. This paper studied automatic object detection to solve these problem by detecting salient region based on Human Visual System. Saliency map is computed using Lab color space which is based on color opposing theory of 'red-green' and 'blue-yellow'. Then Saliency Points are computed from the boundaries of Low-Frequency region that are extracted from Saliency Map. Finally, Rectangle windows are obtained from coordinate value of Saliency Points and these windows are used in GrabCut algorithm to extract objects. Through various experiments, the proposed algorithm computing rectangle windows of salient region and extracting objects has been proved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.61-69
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• 2010

This paper presents an efficient object selection algorithm by analyzing and detecting of human activity. Generally, when people point any something, they will put a face on the target direction. Therefore, the direction of the face and fingers and was ordered to be connected to a straight line. At first, in order to detect the moving objects from the input frames, we extract the interesting objects in real time using background subtraction. And the judgment of movement is determined by Principal Component Analysis and a designated time period. When user is motionless, we estimate the user's indication by estimation in relation to vector from the head to the hand. Through experiments using the multiple views, we confirm that the proposed algorithm can estimate the movement and indication of user more efficiently.

• Journal of the Korea Institute of Building Construction
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• v.10 no.4
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• pp.105-112
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• 2010

The demand for the development of automated construction equipments is gradually increasing to deal with the current problems of construction technology, such as a lack of experienced workers, the aging of engineers, safety issues, etc. In particular, earth work such as excavation is very machine-dependent, and there has been a great deal of research on the development of an intelligent excavator, which involves great safety concerns. Thus, the objective of this study is to develop the technology to enhance the safety of intelligent excavation systems by developing an omnidirectional object detection technology for the intelligent excavator and applying it to a user-friendly system. The existing literature was reviewed, and the function of various sensor technologies was investigated and analyzed. Then, the best laser sensor was selected for an experiment to determine its effectiveness. An omnidirectional object detection algorithm was developed for a user interface program, and this can be used as the fundamental technology for the development of a safety management system for an intelligent excavator.

• Korean Journal of Cognitive Science
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• v.16 no.4
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• pp.333-349
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• 2005

In this paper, we propose a real tine moving object tracking system based on block-based image processing technique and human visual processing. The system has two nun features. First, to take advantage of the merit of the biological mechanism of human retina, the system has two cameras, a CCD(Charge-Coupled Device) camera equipped with wide angle lens for more wide scope vision and a Pan-Tilt-Zoon tamers. Second, the system divides the input image into a numbers of blocks and processes coarsely to reduce the rate of tracking error and the processing time. Tn an experiment, the system showed satisfactory performances coping with almost every noisy image, detecting moving objects very int and controlling the Pan-Tilt-Zoom camera precisely.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.12
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• pp.1267-1279
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• 2014

In this paper, we suggest the development of object detection systems for the safety of the ship through the study of the properties of short-range radar. Many of the short-range radars developed for special purpose like cars has cheaper price advantages but it is not proper to every application. In order to overcome such obstacles we need to analysis data from experiments in various environments and feature analysis of the device is essential. Also, the data clustering algorithms to display correct classified moving objects is necessary. In this paper we propose the advanced fast moving object detection system using short range radars with better detection accuracy. And we proposed a clustering algorithm using the value of the RCS and the speed and trajectory information of the radar data that are reflected.