• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.15-25
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• 2014

In this paper, we propose a loitering detection using trajectory probability distribution and local direction descriptor for intelligent surveillance system. We use a background modeling method for detecting moving object and extract the motion features from each moving object for making feature vectors. After that, we detect the loitering behavior person using K-Nearest Neighbor classifier. We test the proposed method in real world environment and it can achieve real time and robust detection results.

• Smart Media Journal
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• v.11 no.4
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• pp.9-18
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• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2430-2436
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• 2013

This study suggests a method of detecting the still object, which becomes a cause of danger within the crossroad. The Inverse Perspective Transform was performed in order to make the object size consistent by being inputted the real-time image from CCTV that is installed within the crossroad. It established the detection area in the image with the perspective transform and generated the adaptative background image with the use of the moving information on object. The detection of the stop object was detected the candidate region of the stop object by using the background-image differential method. To grasp the appearance of truth on the detected candidate region, a method is proposed that uses the gradient information on image and EHD(Edge Histogram Descriptor). To examine performance of the suggested algorithm, it experimented by storing the images in the commuting time and the daytime through DVR, which is installed on the cross street. As a result of experiment, it could efficiently detect the stop vehicle within the detection region inside the crossroad. The processing speed is shown in 13~18 frame per second according to the area of the detection region, thereby being judged to likely have no problem about the real-time processing.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• Journal of the Korea Safety Management & Science
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• v.22 no.1
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.195-204
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• 2014

This paper presents the depth evaluation for object detection by automated assembling robots. Pattern distortion analysis from a structured light system identifies an object with the greatest depth from its background. An automated assembling robot should prior select and pick an object with the greatest depth to reduce the physical harm during the picking action of the robot arm. Object detection is then combined with a depth evaluation to provide contour, showing the edges of an object with the greatest depth. The contour provides shape information to an automated assembling robot, which equips the laser based proxy sensor, for picking up and placing an object in the intended place. The depth evaluation process using structured light for an automated electronics assembling robot is accelerated for an image frame to be used for computation using the simplest experimental set, which consists of a single camera and projector. The experiments for the depth evaluation process required 31 ms to 32 ms, which were optimized for the robot vision system that equips a 30-frames-per-second camera.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.229-235
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• 2010

In this paper, we propose a method to improve the efficiency of the moving object detection in real-time surveillance camera system. The existing methods, the methods using differential image and background image, are difficult to detect the moving object from outside the video streams. The proposed method keeps the background image if it doesn't be detected moving object using the differential value between a previous frame and a current frame. And the background image is renewed as the moving object is gone in a frame. To decide people and wild pig, the proposed system estimates a bounding box enclosing each moving object in the detecting region. As a result of simulation, the proposed method is better than the existing method.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.2 s.21
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• pp.14-21
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• 2005

This paper describes the system for detecting the tank crew's(loader's) hand, arm, head and the upper half of the body in a danger area between the turret ceiling and the upper breech mechanism by computer vision-based method. This system informs danger of pressed to death to gunner and commander for the safety of operating mission. The camera mounted ort the top portion of the turret ceiling. The system sets search moving object from this image and detects by using change of image, laplacian operator and clustering algorithm in this area. It alarms the tank crews when it's judged that dangerous situation for operating mission. The result In this experiment shows that the detection rate maintains in $81{\sim}98$ percents.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.237-240
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• 2001

An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.