• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.439-445
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• 2021

Low-cost, ultra-high-speed cameras, made possible by the development of image sensors and small displays, can be very useful in image processing and pattern recognition. This paper introduces an algorithm that corrects irregular lighting from a high-speed image that is continuously input with a slight time interval, and which then obtains an exposed skin color region that is the area of interest in a person from the corrected image. In this study, the non-uniform lighting effect from a received high-speed image is first corrected using a frame blending technique. Then, the region of interest is robustly obtained from the input high-speed color image by applying an elliptical skin color distribution model generated from iterative learning in advance. Experimental results show that the approach presented in this paper corrects illumination in various types of color images, and then accurately acquires the region of interest. The algorithm proposed in this study is expected to be useful in various types of practical applications related to image recognition, such as face recognition and tracking, lighting correction, and video indexing and retrieval.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.47-57
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• 2022

Demands for tangible contents using VR/AR technologies are much bigger as contactless services such as sports, physical activity, and fitness are expanded after COVID-19. A variety of technologies such as an offer and analysis of tangible data through a sensor technology, users' physical movement sensing through a motion recognition sensor, a real-time measurement of a physical skeleton point a multiple access to a real-time video, and AI training are being utilized as main technologies. This case study utilized motion recognition technologies as the study on tangible contents necessary for indoor-based physical education, sports, and fitness in the contactless environment and suggested cases to develop the physical measurement contents by design approach for the measurement assessment necessary for the development in tangible contents. The research established lists of the measurement assessment based on professionals' consultations within the measurement assessment function through the test to plan tangible contents and developed tangible contents by reflecting them as assessment measurement elements of tangible contents. The research can be utilized as the design approach of industrial companies which intend to develop tangible contents as well as reference cases of the research on contactless tangible contents for the sports and physical education.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.4
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• pp.129-149
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• 2022

Records have temporal characteristics, including the past and present; linguistic characteristics not limited to a specific language; and various types categorized in a complex way. Processing records such as text, video, and audio in the life cycle of records' creation, preservation, and utilization entails exhaustive effort and cost. Primary natural language processing (NLP) technologies, such as machine translation, document summarization, named-entity recognition, and image recognition, can be widely applied to electronic records and analog digitization. In particular, Korean deep learning-based NLP technologies effectively recognize various record types and generate record management metadata. This paper provides an overview of Korean NLP technologies and discusses considerations for applying NLP technology in records management. The process of using NLP technologies, such as machine translation and optical character recognition for digital conversion of records, is introduced as an example implemented in the Python environment. In contrast, a plan to improve environmental factors and record digitization guidelines for applying NLP technology in the records management field is proposed for utilizing NLP technology.

• Smart Media Journal
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• v.12 no.7
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• pp.18-26
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• 2023

In this study, we propose a cow behavior recognition system based on YOLOX and OC-SORT. YOLO X detects targets in real-time and provides information on cow location and behavior. The OC-SORT module tracks cows in the video and assigns unique IDs. The quantitative analysis module analyzes the behavior and location information of cows. Experimental results show that our system demonstrates high accuracy and precision in target detection and tracking. The average precision (AP) of YOLOX was 82.2%, the average recall (AR) was 85.5%, the number of parameters was 54.15M, and the computation was 194.16GFLOPs. OC-SORT was able to maintain high-precision real-time target tracking in complex environments and occlusion situations. By analyzing changes in cow movement and frequency of mounting behavior, our system can help more accurately discern the estrus behavior of cows.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.47-57
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• 2009

Event detection and recognition is an active and challenging topic recent in Computer Vision. This paper describes a new method for recognizing events caused by human motion from video sequences in an office environment. The proposed approach analyzes human motions using Motion History Image (MHI) sequences, and is invariant to body shapes. types or colors of clothes and positions of target objects. The proposed method has two advantages; one is thant the proposed method is less sensitive to illumination changes comparing with the method using color information of objects of interest, and the other is scale invariance comparing with the method using a prior knowledge like appearances or shapes of objects of interest. Combined with edge detection, geometrical characteristics of the human shape in the MHI sequences are considered as the features. An advantage of the proposed method is that the event detection framework is easy to extend by inserting the descriptions of events. In addition, the proposed method is the core technology for event detection systems based on context-aware computing as well as surveillance systems based on computer vision techniques.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.13-20
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• 2024

This paper introduces a method for generating model images that can identify specific cylindrical medicine containers in videos and investigates data collection techniques. Previous research had separated object detection from specific object recognition, making it challenging to apply automated image stitching. A significant issue was that the coordinate-based object detection method included extraneous information from outside the object area during the image stitching process. To overcome these challenges, this study applies the newly released YOLOv8 (You Only Look Once) segmentation technique to vertically rotating pill bottles video and employs the ORB (Oriented FAST and Rotated BRIEF) feature matching algorithm to automate model image generation. The research findings demonstrate that applying segmentation techniques improves recognition accuracy when identifying specific pill bottles. The model images created with the feature matching algorithm could accurately identify the specific pill bottles.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.21-29
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• 2018

The purpose of this study is to explore supporting strategies for teaching and learning based on students' effect recognition and continuous intention in college flipped learning. It was analyzed 426 data by multivariate analysis of variance (MANOVA) by examining student's effect recognition and continuous intention on 15 flipped learning classes of K-university in Chungnam. The characteristics of learners were male, senior students, students who knew flipped learning, students who did not have previous experience, and students who were learning video at anytime. As a teaching strategy, it was found that effect recognition and continuous intention were high in the supplementary deepening flipped learning class and natural science or engineering area. As a teaching and learning supporting strategies, First, the university should develop and operate flipped class learning strategy program for females and low-grade students. Second, it should support the development of good flipped learning design and operation model of instructor. Third, it should support the development of high quality online learning contents that students can learn from time to time. Fourth, it should support the strengthening of teaching competency to develop and operate flipped learning classes. This study can be used as basic data to support and spread the effective flipped learning classes of the university in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.421-427
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• 2011

The importance of the real-time object recognition and tracking field has been growing steadily due to rapid advancement in the computer vision applications industry. As is well known, the mean-shift algorithm is widely used in robust real-time object tracking systems. Since the mentioned algorithm is easy to implement and efficient in object tracking computation, many say it is suitable to be applied to real-time object tracking systems. However, one of the major drawbacks of this algorithm is that it always converges to a local mode, failing to perform well in a cluttered environment. In this paper, an Optical Flow-based algorithm which fits for real-time recognition of multiple moving objects is proposed. Also in the tests, the newly proposed method contributed to raising the similarity of multiple moving objects, the similarity was as high as 0.96, up 13.4% over that of the mean-shift algorithm. Meanwhile, the level of pixel errors from using the new method keenly decreased by more than 50% over that from applying the mean-shift algorithm. If the data processing speed in the video surveillance systems can be reduced further, owing to improved algorithms for faster moving object recognition and tracking functions, we will be able to expect much more efficient intelligent systems in this industrial arena.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.245-251
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• 2021

As the trend of steadily increasing the number of single or double household, there is a growing demand to see who is the outsider visiting the home during the free time. Various models of face recognition technology have been proposed through many studies, and Harr Cascade of OpenCV and Hog of Dlib are representative open source models. Among the two modes, Dlib's Hog has strengths in front of the indoor and at a limited distance, which is the focus of this study. In this paper, a face recognition visitor access system based on Dlib was designed and implemented. The whole system consists of a front module, a server module, and a mobile module, and in detail, it includes face registration, face recognition, real-time visitor verification and remote control, and video storage functions. The Precision, Specificity, and Accuracy according to the change of the distance threshold value were calculated using the error matrix with the photos published on the Internet, and compared with the results of previous studies. As a result of the experiment, it was confirmed that the implemented system was operating normally, and the result was confirmed to be similar to that reported by Dlib.

• Annual Conference of KIPS
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• 2006.05a
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• pp.697-700
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• 2006

본 논문에서는 동작에 근거한 새로운 비디오 편집 방법을 제안한다. 강의 비디오에서 전자 슬라이드 내용을 자동으로 검출하고 비디오와 동기화한다. 각 동기화된 표제의 동작을 연속적으로 추적 및 인식한 후, 등록된 화면과 슬라이드에서 변환 내용을 찾아 동작이 일어 나는 영역을 확인한다. 인식된 동작과 등록된 지점에서 슬라이드의 정보를 추출하여 슬라이드 영역을 부분적으로 확대한다거나 원본 비디오를 자동으로 편집함으로써 비디오의 질을 향상 시킬 수가 있다. 2 개의 비디오 가지고 실험한 결과 각각 95.5, 96.4%의 동작 인식 결과를 얻을 수 있었다.