• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.420-425
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• 2009

In this paper, image detection and recognition algorithms are studied with respect to embedded carrier system. There are many suggested techniques to detect and recognize objects. But they have the propensity to need much calculation for high hit rate. Advanced and modified method needs to study for embedded systems that low power consumption and real time response are requested. The proposed methods were implemented using Intel(R) Open Source Computer Vision Library provided by Intel Corporation. And they run and tested on embedded system using a ARM920T processor by cross-compiling. They showed 1.6sec response time and 95% hit rate and supported the automated moving carrier system smoothly.

• Journal of the Optical Society of Korea
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• v.13 no.4
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• pp.406-415
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• 2009

Optical scanning holography (OSH) is a distinct digital holographic technique in that real-time holographic recording a three-dimensional (3-D) object can be acquired by using two-dimensional active optical heterodyne scanning. Applications of the technique so far have included optical scanning cryptography, optical scanning microscopy, 3-D pattern recognition, 3-D holographic TV, and 3-D optical remote sensing. This paper reviews some of the recent progress in OSH. Some possible further works are also discussed.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.182-189
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• 2014

This paper proposed a framework of recognition and tracking for underwater objects using sonar images as an alternative of underwater optical camera which has the limitation of usage due to turbidity. In Part 1, a design and recognition method for 2D artificial landmark was proposed considering the practical performance of current imaging sonars. In particular, its materials are selected in order to maximize detectability based on characteristics of imaging sonar and ultrasonic waves. It has a simple and omni-directional shape which allows an easy modeling of object, and it includes region based features as identifications. Also, we proposed a real-time recognition algorithm including edge detector, Hough circle transforms, and shape matrix based recognition algorithm. The proposed methods are verified by basin tests using DIDSON.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1153-1164
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• 2012

In this paper, we propose an efficient car management system based on object-oriented modeling using car number recognition and smart phone. The proposed system perceives car number of repair vehicle after recognizing the licence plate using an IP camera in real time. And then, existing repair history information of the recognized car is be displayed in DID. In addition, maintenance process is shooting video while auto maintenance mechanic repairs car through IP-camera. That will be provide customer car identification and repairs history management function by sending key frames extracted from recorded video automatically. We provide user graphic interface based on web and mobile for your convenience. The module design of the proposed system apply software design modeling based on granular object-oriented considering reuse and extensibility after implementation. Car repairs center and maintenance companies can improve business efficiency, as well as the requested vehicle repair can increase customer confidence.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.25-35
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• 2016

Since a sophisticated location determination technology (LDT) is necessary for accurate positioning in urban area environments, numerous studies related to the LDT using the RFID (Radio Frequency IDentification) technology have been implemented for real-time positioning and data transferring. However, there are still lots of unsolved questions especially regarding what to use as base stations and what are corresponding results under the intrinsic complexity of alignment and configuration of components used for the RFID positioning. This study proposes the street light fixtures as base stations where the RFID receivers will be embedded for the mobile tracking scheme. As street light fixtures are usually installed at a certain distance interval, they can be used as base stations for the RFID receiver installation. Using the principle of the single row triangle network, the RFID receiver organization is determined based on the experiments such as recognition distance measurement and tag position accuracy estimation at inside and outside of the single row triangle network. The results verify that the mobile tracking scheme which uses RFID-embedded street light fixtures, suggested and configured in this study, is effective for the real-time outdoor positioning.

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.31-38
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• 2011

This paper introduces a video based traffic monitoring system for detecting vehicles and obstacles on the road. To segment moving objects from image sequence, we adopt the background subtraction algorithm based on the local binary patterns (LBP). Recently, LBP based texture analysis techniques are becoming popular tools for various machine vision applications such as face recognition, object classification and so on. In this paper, we adopt an extension of LBP, called the Diagonal LBP (DLBP), to handle the background subtraction problem arise in vision-based autonomous parking systems. It reduces the code length of LBP by half and improves the computation complexity drastically. An edge based shadow removal and blob merging procedure are also applied to the foreground blobs, and a pose estimation technique is utilized for calculating the position and heading angle of the moving object precisely. Experimental results revealed that our system works well for real-time vehicle localization and tracking applications.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.187-188
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• 2021

최근 COVID19 상황에서 생활 속 거리두기가 강조되면서 관광지나 다중이용시설 등의 이용객 수와 밀집도를 파악하는 것이 중요해지고 있다. 따라서, CCTV 영상을 활용하여 저렴한 비용으로 다중이용시설의 출입자수에 대한 정보를 실시간으로 모니터링할 수 있는 시스템이 필요하다. 이를 위해 본 논문에서는 딥러닝 실시간 객체인식기술을 활용한 출입자의 수와 동선을 측정하여 출입자에 대한 통계정보를 웹브라우저를 통해 제공하는 시스템을 개발하였다. 실시간 객체인식기술인 YOLOv4와 YOLOv4-tiny 알고리즘을 Nvidia사의 Jetson AGX Xavier 와 데스크톱PC에 적용하여 각 알고리즘의 FPS와 객체 인식률을 비교 분석 하여 알고리즘을 적용하였다.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.2_1
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• pp.217-223
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• 2023

With the rapid advancement of technologies, need for different research fields where this technology can be used is also increasing. One of the most researched topic in computer vision is object detection, which has widely been implemented in various fields which include healthcare, video surveillance and education. The main goal of object detection is to identify and categorize all the objects in a target environment. Specifically, methods of object detection consist of a variety of significant techniq ues, such as image processing and patterns recognition. Anomaly detection is a part of object detection, anomalies can be found various scenarios for example crowded places such as subway stations. An abnormal event can be assumed as a variation from the conventional scene. Since the abnormal event does not occur frequently, the distribution of normal and abnormal events is thoroughly imbalanced. In terms of public safety, abnormal events should be avoided and therefore immediate action need to be taken. When abnormal events occur in certain places, real time detection is required to prevent and protect the safety of the people. To solve the above problems, we propose a modified YOLOv5 object detection algorithm by implementing dilated convolutional layers which achieved 97% mAP50 compared to other five different models of YOLOv5. In addition to this, we also created a simple mobile application to avail the abnormal event detection on mobile phones.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.313-321
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• 2017

One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than $4.5^{\circ}$ in real-time.