• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.804-814
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• 2009

In this paper, a novel system for avatar motion controlling by tracking face is presented. The system is composed of three main parts: firstly, LCS (Local Cluster Searching) method based face feature detection algorithm, secondly, HMM based feature points recognition algorithm, and finally, avatar controlling and animation generation algorithm. In LCS method, face region can be divided into many small piece regions in horizontal and vertical direction. Then the method will judge each cross point that if it is an object point, edge point or the background point. The HMM method will distinguish the mouth, eyes, nose etc. from these feature points. Based on the detected facial feature points, the 3D avatar is controlled by two ways: avatar orientation and animation, the avatar orientation controlling information can be acquired by analyzing facial geometric information; avatar animation can be generated from the face feature points smoothly. And finally for evaluating performance of the developed system, we implement the system on Window XP OS, the results show that the system can have an excellent performance.

• Journal of IKEEE
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• v.14 no.4
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• pp.317-323
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• 2010

The video surveillance system is adopted in many places due to its efficiency and constancy in monitoring a specific area over a long period of time. However, many surveillance systems composed of a single static camera often produce unsatisfactory results due to their lack of field of view. In this paper, we present a video surveillance system based on wide baseline stereo cameras to overcome the limitation. We adopt the codebook algorithm and mathematical morphology to robustly model the foreground pixels of the moving object in the scene and calculate the trajectory of the moving object via 3D reconstruction. The experimental results show that the proposed system detects a moving object and generates a top view trajectory successfully to track the location of the object in the world coordinates.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.745-755
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• 2023

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipment of the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedingly crucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining to the flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementioned problems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an object based on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defects outside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of small sample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detection and solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidents occasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wire rope defect detection, the average correctness rate and the average accuracy rate of the model are significantly enhanced with those before the modification, and that the detection speed can be maintained at a real-time level.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.155-170
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• 2020

Passive ranging is a critical part of machine vision measurement. Most of passive ranging methods based on machine vision use binocular technology which need strict hardware conditions and lack of universality. To measure the distance of an object placed on horizontal plane, we present a passive ranging method based on monocular vision system by smartphone. Experimental results show that given the same abscissas, the ordinatesis of the image points linearly related to their actual imaging angles. According to this principle, we first establish a depth extraction model by assuming a linear function and substituting the actual imaging angles and ordinates of the special conjugate points into the linear function. The vertical distance of the target object to the optical axis is then calculated according to imaging principle of camera, and the passive ranging can be derived by depth and vertical distance to the optical axis of target object. Experimental results show that ranging by this method has a higher accuracy compare with others based on binocular vision system. The mean relative error of the depth measurement is 0.937% when the distance is within 3 m. When it is 3-10 m, the mean relative error is 1.71%. Compared with other methods based on monocular vision system, the method does not need to calibrate before ranging and avoids the error caused by data fitting.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5496-5521
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• 2018

Object detection and tracking is the basic capability of mobile robots to achieve natural human-robot interaction. In this paper, an object tracking system of mobile robot is designed and validated using improved multiple instance learning algorithm. The improved multiple instance learning algorithm which prevents model drift significantly. Secondly, in order to improve the capability of classifiers, an active sample selection strategy is proposed by optimizing a bag Fisher information function instead of the bag likelihood function, which dynamically chooses most discriminative samples for classifier training. Furthermore, we integrate the co-training criterion into algorithm to update the appearance model accurately and avoid error accumulation. Finally, we evaluate our system on challenging sequences and an indoor environment in a laboratory. And the experiment results demonstrate that the proposed methods can stably and robustly track moving object.

• Journal of IKEEE
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• v.27 no.3
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• pp.280-287
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• 2023

Deep learning advancements can solve computer vision problems, but large-scale datasets are necessary for high accuracy. In this paper, we propose an image generation technique using object bounding boxes and image edge components. The object bounding boxes are extracted from the images through object detection, and image edge components are used as input values for the image generation model to create new image data. As results of experiments, the images generated by the proposed method demonstrated similar image quality to the source images in the image quality assessment, and also exhibited good performance during the deep learning training process.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.44-53
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• 2005

In this paper, a improved moving object detection algorithm for stable performance of surveillance system in case of iterative moving in limited area and rapidly illuminance change in background scene is proposed. The proposed algorithm is that background scenes are sampled for initializing background image then the sampled fames are divided by block and sum of graylevel value for each block pixel was calculated, respectively. The initialization of background image is that background frame is respectively reconstructed with selecting only the maximum graylevel value and the minimum graylevel value of blocks located at same position between adjacent frames, then reference images of background are set by the reconstructed background images. Moving object detecting is that the current image frame is divided by block then sum of graylevel value for each block pixel is calculated. If the calculated value is out of graylevel range of the initialized two reference images, it is decided with moving objects block, otherwise it is decided background. The evaluated results is that the error rate of the proposed method is less than the error rate of the existing methods from $0.01{\%}$ to $20.33{\%}$ and the detection rate of the proposed method is better than the existing methods from $0.17{\%}\;to\;22.83{\%}$.

• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.9-19
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• 2024

In this paper, a real-time public transportation monitoring system is proposed. The proposed system was implemented by developing a public transportation app and utilizing optical sensors, pressure sensors, and an object detection algorithm. Additionally, a bus model was created to verify the system's functionality. The proposed real-time public transportation monitoring system has three key features. First, the app can monitor congestion levels within public transportation by detecting seat occupancy and the total number of passengers based on changes in optical and pressure sensor readings. Second, to prevent errors in the optical sensor that can occur when multiple passengers board or disembark simultaneously, we explored the possibility of using the YOLO object detection algorithm to verify the number of passengers through CCTV footage. Third, convenience is enhanced by displaying occupied seats in different colors on a separate screen. The system also allows users to check their current location, available public transportation options, and remaining time until arrival. Therefore, the proposed system is expected to offer greater convenience to public transportation users.