• Journal of Platform Technology
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• v.11 no.3
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• pp.56-67
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• 2023

Recently, de-identification of personal information, which has been a long-cherished desire of the data-based industry, was revised and specified in August 2020. It became the foundation for activating data called crude oil[2] in the fourth industrial era in the industrial field. However, some people are concerned about the infringement of the basic rights of the data subject[3]. Accordingly, a development study was conducted on the Batch De-Identification Tool, a personal information de-identification automation tool. In this study, first, we developed an image labeling tool to label human faces (eyes, nose, mouth) and car license plates of various resolutions to build data for training. Second, an object recognition model was trained to run the object recognition module to perform de-identification of personal information. The automated personal information de-identification tool developed as a result of this research shows the possibility of proactively eliminating privacy violations through online services. These results suggest possibilities for data-based industries to maximize the value of data while balancing privacy and utilization.

• 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.

• 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.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.189-198
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• 2023

As crimes frequently occur on the street, the spread of CCTV is increasing. However, due to the shortcomings of passively operated CCTV, the need for intelligent CCTV is attracting attention. Due to the heavy system of such intelligent CCTV, high-performance devices are required, which has a problem in that it is expensive to replace the general CCTV. To solve this problem, an intelligent CCTV system that recognizes low-quality images and operates even on devices with low performance is required. Therefore, this paper proposes a Saying CCTV system that can detect threats in real time by using the AWS cloud platform to lighten the system and convert images into text. Based on the data extracted using YOLO v4 and OpenPose, it is implemented to determine the risk object, threat behavior, and threat situation, and calculate the risk using machine learning. Through this, the system can be operated anytime and anywhere as long as the network is connected, and the system can be used even with devices with minimal performance for video shooting and image upload. Furthermore, it is possible to quickly prevent crime by automating meaningful statistics on crime by analyzing the video and using the data stored as text.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1076-1084
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• 2019

In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.1-10
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• 2019

Object-based image matching algorithms have been widely used in the image processing and computer vision fields. A variety of applications based on image matching algorithms have been recently developed for object recognition, 3D modeling, video tracking, and biomedical informatics. One prominent example of image matching features is the Scale Invariant Feature Transform (SIFT) scheme. However many applications using the SIFT algorithm have implemented based on stand-alone basis, not client-server architecture. In this paper, We initially implemented based on client-server structure by using SIFT algorithms to identify and match objects in biomedical images to provide useful information to the user based on the recently released Mobile platform. The major methodological contribution of this work is leveraging the convenient user interface and ubiquitous Internet connection on Mobile device for interactive delineation, segmentation, representation, matching and retrieval of biomedical images. With these technologies, our paper showcased examples of performing reliable image matching from different views of an object in the applications of semantic image search for biomedical informatics.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.968-975
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• 2023

Purpose: We would like to confirm that the false positive rate of flames/smoke is high when detecting fires. Propose a method and dataset to recognize and classify fire situations to reduce the false detection rate. Method: Using the video as learning data, the characteristics of the fire situation were extracted and applied to the classification model. For evaluation, the model performance of Yolov8 and Slowfast were compared and analyzed using the fire dataset conducted by the National Information Society Agency (NIA). Result: YOLO's detection performance varies sensitively depending on the influence of the background, and it was unable to properly detect fires even when the fire scale was too large or too small. Since SlowFast learns the time axis of the video, we confirmed that detects fire excellently even in situations where the shape of an atypical object cannot be clearly inferred because the surrounding area is blurry or bright. Conclusion: It was confirmed that the fire detection rate was more appropriate when using a video-based artificial intelligence detection model rather than using image data.

• Annual Conference of KIPS
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• 2021.11a
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• pp.1188-1191
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• 2021

The function of the black box can be combined with a car to check the video through a cloud server, reduce the hassle of checking the video through a memory card, check the black box image in real time through a PC and smartphone, and check the user's Excel, brake operation status, and handle control record at the time of the accident. In addition, the goal was to accurately identify vehicle accidents and simplify accident handling through artificial intelligence object recognition of black box images using cloud services. Measures can be prepared to preserve images even if the black box itself loses, such as fire, flooding, or damage that occurs in an accident. It has been confirmed that the exact situation before and after the accident can be grasped immediately by providing object recognition and log recording functions under actual driving experimental conditions.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.77-89
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• 2024

This paper presents an unmanned store management system that can provide inventory management and theft prevention for displayed products using a small camera that can monitor the shelves of sold products in small and medium-sized stores. This system is a service solution that integrates object recognition, real-time communication, security management, access management, and mobile authentication. The proposed system uses a custom YOLOv5-x model to recognize objects on the display, measure quantities in real time, and support real-time data communication with servers through Raspberry Pie. In addition, the number of objects in the database and the object recognition results are compared to detect suspected theft situations and provide burial images at the time of theft. The proposed unmanned store solution is expected to improve the efficiency of small and medium-sized unmanned store operations and contribute to responding to theft.