• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.60-66
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• 2012

In this paper, we propose abandoned object detection algorithm. When abandoned object was occluded other object, the existing methods cannot detect abandoned object because those methods are not able to estimate the location of abandoned object. In order to overcome this problem, the proposed algorithm extracts the corners around abandoned object. The detected corners are linked to center of abandoned object called by supporters. We can then estimate the location of abandoned object by using supporters. Therefore, the proposed algorithm can detect and estimate the location of abandoned object, when abandoned object is occluded by other object. For this reason, the proposed algorithm can be applied to intelligent surveillance system to prevent bomb terror, which disguises as luggage or box.

• Journal of Biomedical Engineering Research
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• v.43 no.3
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• pp.131-135
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• 2022

Due to COVID-19, Correct method of wearing mask is important to prevent COVID-19 and the other respiratory tract infections. And the deep learning technology in the image processing has been developed. The purpose of this study is to create the type of mask wearing dataset for deep learning models and select the deep learning model to detect the wearing mask correctly. The Image dataset is the 2,296 images acquired using a web crawler. Deep learning classification models provided by tensorflow are used to validate the dataset. And Object detection deep learning model YOLOs are used to select the detection deep learning model to detect the wearing mask correctly. In this process, this paper proposes to validate the type of mask wearing datasets and YOLOv5 is the effective model to detect the type of mask wearing. The experimental results show that reliable dataset is acquired and the YOLOv5 model effectively recognize type of mask wearing.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.365-371
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• 2009

A intelligent mobile hexapod 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%. 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. 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.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.597-600
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• 1999

In this paper, we propose a fast object-tracking algorithm in a moving picture. The proposed object-tracking algorithm is based on a projection scheme. More specifically, to alleviate the computational complexities of the previous motion estimation methods, we propose to use the projected row and column 1-D image data to extract the motion information. Experimental results show that the proposed method can detect the motion of an object fairly well with reduced computational time.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.427-430
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• 2019

Object detection is applied in various field. Autonomous driving, surveillance, OCR(optical character recognition) and aerial image etc. We will look at the algorithms that are using to object detect. These algorithms are divided into two methods. The one is R-CNN algorithms [2], [5], [6] which based on region proposal. The other is YOLO [7] and SSD [8] which are one stage object detector based on regression/classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.646-648
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• 2012

In this paper, an image detection technique is proposed to extract image features in a mobile terminal environment. To detect objects, the HSI color model of the image is used. The object's corner points are detected using the Harris corner detection method. Finally we detect the object of interest using region growing The experiment results show that the proposed method improves detection performance and reduces the amount of computation.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.558-570
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• 2018

Recently, object detection is a critical function for any system that uses computer vision and is widely used in various fields such as video surveillance and self-driving cars. However, the conventional methods can not detect the objects clearly because of the dynamic background change in the beach. In this paper, we propose a new technique to detect humans correctly in the dynamic videos like shores. A new background modeling method that combines spatial GMM (Gaussian Mixture Model) and temporal GMM is proposed to make more correct background image. Also, the proposed method improve the accuracy of people detection by using SVM (Support Vector Machine) to classify people from the objects and KCF (Kernelized Correlation Filter) Tracker to track people continuously in the complicated environment. The experimental result shows that our method can work well for detection and tracking of objects in videos containing dynamic factors and situations.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.173-178
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• 2019

Mongolian historical and cultural monuments on settlement areas of stone inscriptions, stone images, rock-drawings, remains of cities, architecture are still telling us their stories. These monuments depict the understanding of the word, philosophical and artistic outlook, beliefs, religion, national art, language, culture and traditions of Mongols [1]. Nowadays computer science, especially computer vision is applying in the other science fields. The main problem is how to apply and which algorithm can detect and classify the objects correctly. In this paper, we propose a method to detect object from Mongolian nomadic environment images. This work proposes a method for object detection that is the combination of the binary operations in the edge detection results. We found out the best method and parameters of state-of-the-art machine learning algorithms. In experimental result, we evaluate our results with 10-fold cross validation and split 66% strategies.

• Journal of Biomedical Engineering Research
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• v.13 no.2
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• pp.115-124
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• 1992

An algorithm has been proposed for the automatic detection of optimal epiand endocardial left ventricular borders from 2-D short axis echocardiogram which is degraded by noise and echo drop out. For the implementation of the algorithm, we modified Ballard's Generalized Hough Transform which can be applicable only for deterministic object border, and newly proposed Fuzzy Hough Transform method. The algorithm presented here allows detection of object whose exact shapes are unknown. The algorithm only requires an approximate model of target object based on anatomical data. To detect the approximate epicardial contour of left ventricle, Fuzzy Hough Transform was applied to the echocardiogram. The optimal epicardial contour was founded by using graph searching method which contains cost function analysis process. Using this optimal epicardial contour and average thickness imformation of left ventricular wall, the approximate endocardial line was founded, and graph searching method was also used to detect optimal endocardial contour.

• The Journal of Information Technology
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• v.10 no.4
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• pp.27-39
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• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.