• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.143-148
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• 2012

There are many researches on segmentation of sidescan sonar image to recognize or classify the underwater objects. Although existing algorithms's performance is good in detecting object's shadow and reducing the underwater noise, the computing time is very low. In this paper we try to separate shadow from background and segment the underwater image by using morphological method using background's noise distribution characteristics and object's shadow charateristics. This algorithm is useful when the average of background is lower than the average of the shadow, because this is adjusted from the background's chracteristics. Results shows that the algorithm works fine in multiple object environments and the computing time is reduced to 1 second.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.731-736
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• 2008

In this paper, we propose the movement detection algorithm by using virtual skeleton model. To do this, first, we eliminate error values by using conventioanl method based on RGB color model and eliminate unnecessary values by using the HSI color model. Second, we construct the virtual skeleton model with skeleton information of 10 peoples. After matching this virtual model to original image, we extract the real head silhouette by using the proposed circle searching method. Third, we extract the object by using the mean-shift algorithm and this head information. Finally, we validate the applicability of the proposed method through the various experiments in a complex environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.477-486
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• 2016

This paper presents the area classification, identification, and tracking for multiple moving objects with the similar colors. To do this, first, we use the GMM(Gaussian Mixture Model)-based background modeling method to detect the moving objects. Second, we propose the use of the binary and morphology of image in order to eliminate the shadow and noise in case of detection of the moving object. Third, we recognize ROI(region of interest) of the moving object through labeling method. And, we propose the area classification method to remove the background from the detected moving objects and the novel method for identifying the classified moving area. Also, we propose the method for tracking the identified moving object using Kalman filter. To the end, we propose the effective tracking method when detecting the multiple objects with the similar colors. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.69-74
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• 2009

Abandoned objects should be treated as possibly dangerous things for public areas until they turn out to be safe because explosive material or chemical substance is intentionally contained in them for public terrors. For large public areas such as airports or train stations, there are limits in man-power for security staffs to check all the monitors for covering the entire area under surveillance. This is the basic motivation of developing the automatic detection system for dangerous abandoned objects based on vision technology. In this research, well-known DBE is applied to stably extract background images and the HOG algorithm is adapted to discriminate between human and stuff for object classification. To show the effectiveness of the proposed system, experiments are carried out in detecting intrusion for a forbidden area and alarming for abandoned objects in a room under surveillance.

• Smart Media Journal
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• v.5 no.3
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• pp.93-98
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• 2016

The literal meaning of loitering is "to lingering aimlessly or as if aimless in or about a place". And most criminals show this kind of act before they actually commit crime. Therefore, detecting this kind of loitering can effectively prevent a variety of crime. In this paper, we propose a loitering-detection algorithm using the Raspberry Pi. Proposed algorithm uses an adaptive difference image to detect moving objects and morphology opening operation to enhance the accuracy of detection. The loitering- behavior is being detected by using the center of gravity of the object to see the changes of angle; and pixel movement distance to determine the height of the object. When the loitering-behavior is detected, it outputs the alarm to tell the users by using the Raspberry Pi.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1345-1360
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• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.555-562
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• 2021

In this paper, we propose a sorting method for extracting accurate license plate information, which is currently used in Korea, after detecting objects using YOLO. We propose sorting methods for the five types of vehicle license plates managed by the Ministry of Land, Infrastructure and Transport by classifying the plates with the number of lines, Korean characters, and numbers. The results of experiments with 5 license plates show that the proposed algorithm identifies all license plate types and information by focusing on the object with high reliability score in the result label file presented by YOLO and deleting unnecessary object information. The proposed method will be applicable to all systems that recognize license plates.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.536-546
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• 2022

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

• Journal of Drive and Control
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• v.19 no.3
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• pp.9-15
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• 2022

This paper proposes a method for constructing and verifying datasets used in deep learning technology, to prevent safety accidents in automated construction machinery or autonomous vehicles. Although open datasets for developing image recognition technologies are challenging to meet requirements desired by users, this study proposes the interface of virtual simulators to facilitate the creation of training datasets desired by users. The pixel-level training image dataset was verified by creating scenarios, including various road types and objects in a virtual environment. Detecting an object from an image may interfere with the accurate path determination due to occlusion areas covered by another object. Thus, we construct a database, for developing an occlusion area detection algorithm in a virtual environment. Additionally, we present the possibility of its use as a deep learning dataset to calculate a grid map, that enables path search considering occlusion areas. Custom datasets are built using the RDBMS system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.109-111
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• 2021

Recently, computer vision research using light field cameras has been actively conducted. Since light field cameras have spatial information, various studies are being conducted in fields such as depth map estimation, super resolution, and 3D object detection. In this paper, we propose a method for detecting objects in blur images through a 7×7 array of images acquired through a light field camera. The blur image, which is weak in the existing camera, is detected through the light field camera. The proposed method uses the SSD algorithm to evaluate the performance using blur images acquired from light field cameras.