• Journal of Game and Entertainment
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• v.2 no.2
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• pp.70-74
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• 2006

In this paper I present a system that with infrared vision sense tracks the position and orientation of a wireless object on a tabletop display surface. The system offers two types of improvements over existing computer vision tracking approaches. First, the system tracks an object accurately without susceptibility to changes in lighting conditions. Second, the system tracks not only the orientation but button click state of the object. This system can detect these changes in real time. Finally, I present an application of the system : Color Lab Box.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.116-125
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• 2007

In this paper, we propose a illumination environment adaptive real-time surveillance system for security of important area such as military bases, prisons, and strategic infra structures. The proposed system recognizes movement of objects on the bright environments as well as in dark illumination. The procedure of proposed system may be summarized as follows. First, the system discriminates between bright and dark with input image distribution. Then, if the input image is dark, the system has a pre-processing. The Multi-scale Retinex Color Restoration(MSRCR) is processed to enhance the contrast of image captured in dark environments. Secondly, the enhanced input image is subtracted with the revised background image. And then, we take a morphology image processing to obtain objects correctly. Finally, each bounding box enclosing each objects are tracked. The center point of each bounding box obtained by the proposed algorithm provides more accurate tracking information. Experimental results show that the proposed system provides good performance even though an object moves very fast and the background is quite dark.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.293-297
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• 2021

This paper applies the natural history contents of the Science Museum and combines the Gestalt theory to develop the butterfly arrangement structure of the butterfly sample box and the butterfly sample information necessary for the sample box as AR (Augmented Reality). Existing analog sample information is expressed as digital information by combining place, butterfly information, and graph to maximize the effect of digital diorama exhibition. Digital natural history information is increased or decreased, and an environment optimized for real samples and suitability is constructed, and natural history contents are arranged in the principles of collectiveness, closure, simplicity, and continuity using the Gestalt visual perception principle to increase attention and increase the attention of butterfly collection information. Was applied as an application plan of AR.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.133-146
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• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.136-145
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• 2019

The crowded environment of a domestic pig farm is highly vulnerable to the spread of infectious diseases such as foot-and-mouth disease, and studies have been conducted to automatically analyze behavior of pigs in a crowded pig farm through a video surveillance system using a camera. Although it is required to correctly separate occluding pigs for tracking each individual pigs, extracting the boundaries of the occluding pigs fast and accurately is a challenging issue due to the complicated occlusion patterns such as X shape and T shape. In this study, we propose a fast and accurate method to separate occluding pigs not only by exploiting the characteristics (i.e., one of the fast deep learning-based object detectors) of You Only Look Once, YOLO, but also by overcoming the limitation (i.e., the bounding box-based object detector) of YOLO with the test-time data augmentation of rotation. Experimental results with two-pigs occlusion patterns show that the proposed method can provide better accuracy and processing speed than one of the state-of-the-art widely used deep learning-based segmentation techniques such as Mask R-CNN (i.e., the performance improvement over Mask R-CNN was about 11 times, in terms of the accuracy/processing speed performance metrics).

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.167-175
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• 2020

In this paper, we propose a traffic accident detection on vehicle-mounted camera. In the proposed method, the minimum bounding box coordinates the central coordinates on the bird's eye view and motion vectors of each vehicle object, and ego-motions of the vehicle equipped with dash-cam are extracted from the dash-cam video. By using extracted 4 kinds features as the input of Bi-LSTM (bidirectional LSTM), the accident probability (score) is predicted. To investigate the effect of each input feature on the probability of an accident, we analyze the performance of the detection the case of using a single feature input and the case of using a combination of features as input, respectively. And in these two cases, different detection models are defined and used. Bi-LSTM is used as a cascade, especially when a combination of the features is used as input. The proposed method shows 76.1% precision and 75.6% recall, which is superior to our previous work.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of the Korea Computer Graphics Society
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• v.21 no.5
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• pp.11-19
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• 2015

Our research introduces a novel system for creating 3D vehicle animation. Our system is for intuitively authoring vehicle accident scenes according to videos or based on user-drawn trajectories. Our system has been implemented by combining three existing ideas. The first part is for obtaining 3D trajectory of a vehicle from black-box videos. The second part is a tracking algorithm that controls a vehicle to follow a given trajectory with small errors. The last part optimizes the vehicle control parameters so that the error between the input trajectory and simulated vehicle trajectory is minimized. We also simulate the deformation of the car due to an impact to achieve believable results in real-time.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.86-91
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• 2020

As the number of elderly people living alone increases, the frequency of fall accidents is also increasing. Falls are a threat to the health of older adults and can reduce their ability to remain independent. To solve this problem, we need real-time technology to recognize and respond to the critical condition of the elderly living alone. Therefore, this paper proposes a modified fall detection algorithm based on YOLO-KCF that can check one of the emergency situations in real time for the elderly living alone. YOLO can detect not only the detection of objects, but also the behavior of objects, namely stand and fall. Therefore, this paper can detect fall using the ratio of change of boundary box between stand and falling situation, and this algorithm can improve the shortcomings of KCF.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.63-70
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• 2023

Due to the spread of high-definition black boxes and the introduction of mobile applications such as 'Smart Citizens Report' and 'Safety Report', the number of public interest reports for violations of Traffic Law has increased rapidly, resulting in shortage of police personnel to handle them. In this paper, we describe the development of a system that can automatically detect lane violations which account for the largest proportion of public interest reporting videos for violations of traffic laws, using deep learning algorithms. In this study, a method for recognizing a vehicle and a solid line object using a YOLO model and a Lanenet model, a method for tracking an object individually using a deep sort algorithm, and a method for detecting lane change violations by recognizing the overlapping range of a vehicle object's bounding box and a solid line object are described. Using this system, it is expected that the shortage of police personnel in charge will be resolved.