• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.2036-2043
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• 2016

The $360^{\circ}$ Virtual Reality (VR) live-action movies are filmed by attaching multiple cameras to a rig to shoot the images omni-directionally. Especially, for a live-action film that requires a variety of scenes, the director of photography and his staff usually have to operate the rigged cameras directly all around the scene and edit the footage during the post-production stage so that the entire process can incur much time and high cost. However, it will also be possible to acquire high-quality omni-directional images with fewer staff if the camera rig(s) can be controlled remotely to allow more flexible camera walking. Thus, a $360^{\circ}$ VR filming system with remote-controlled camera rig has been proposed in this study. The movie producers will be able to produce the movies that provide greater immersion with this system.

• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.49-54
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• 2020

Speed enforcement is one of the major challenges in traffic safety. The increasing number of accidents and fatalities has led governments to respond by implementing an intelligent control system. For example, the Korean government implemented a speed camera system for maintaining road safety. However, many drivers still engage in speeding behavior in blackspot areas where speed cameras are not provided. Therefore, we propose a methodology to analyze the combined accident and speeding data to offer recommendations to maintain traffic safety. We investigate three factors: "section," "existing speed camera location," and "over speeding data." To interpret the results, we used the QGIS tool for visualizing the spatial distribution of the incidents. Finally, we provide four recommendations based on the three aforementioned factors: "investigate with experts," "no action," "install fixed speed cameras," and "deploy mobile speed cameras."

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.97-108
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• 2022

The video-assisted human action recognition [1] field is one of the most active ones in computer vision research. Since the depth data [2] obtained by Kinect cameras has more benefits than traditional RGB data, research on human action detection has recently increased because of the Kinect camera. We conducted a systematic study of strategies for recognizing human activity based on deep data in this article. All methods are grouped into deep map tactics and skeleton tactics. A comparison of some of the more traditional strategies is also covered. We then examined the specifics of different depth behavior databases and provided a straightforward distinction between them. We address the advantages and disadvantages of depth and skeleton-based techniques in this discussion.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.491-496
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• 2003

This paper proposes modeling of human behavior and a method of finding irregular human behavior. At first, human behavior model is proposed by paying attention to habitual human behavior at home. Generally, it is difficult to obtain the information of individual life pattern because of high cost for setting sensors such as cameras to observe human action. Therefore we capture turning on/off consumer electronic equipments as actual human behavior action, where some or many consumer electric equipments were used such as television, room light, video and so on in our daily life. Noting that are some relations between turning on/off those consumer electric equipments and our action, we proposes how to construct a human behavior knowledge by analyzing human behavior based on observation of human habitual life. Also an algorithm to identify on find irregular behavior different from habitual life behavior are described. Finally, the significance of the proposed method is shown by some experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.559-575
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• 2023

3D human pose estimation is widely applied in various fields, including action recognition, sports analysis, and human-computer interaction. 3D human pose estimation has achieved significant progress with the introduction of convolutional neural network (CNN). Recently, several researches have proposed the use of multiview approaches to avoid occlusions in single-view approaches. However, as the number of cameras increases, a 3D pose estimation system relying on a CNN may lack in computational resources. In addition, when a single host system uses multiple cameras, the data transition speed becomes inadequate owing to bandwidth limitations. To address this problem, we propose a distributed real-time 3D pose estimation framework based on asynchronous multiple cameras. The proposed framework comprises a central server and multiple edge devices. Each multiple-edge device estimates a 2D human pose from its view and sendsit to the central server. Subsequently, the central server synchronizes the received 2D human pose data based on the timestamps. Finally, the central server reconstructs a 3D human pose using geometrical triangulation. We demonstrate that the proposed framework increases the percentage of detected joints and successfully estimates 3D human poses in real-time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.75-81
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• 2024

This paper focuses on developing a first-person 3D game to provide extreme fear to players through realistic camera direction utilizing the features of action cameras. As a new camera production technique, we introduce perspective distortion using a wide-angle lens and camera shake when moving to provide higher immersion than existing games. The theme of the game is horror room escape, and the player starts with a firearm, but in order to overcome the concern that the game's difficulty is low due to the use of firearms, the player is asked to control the use of firearms by imposing burdens such as chasing monsters and reducing the number of magazines. The significance of this paper is that we developed a new type of 3D game that maximizes the fear effect of players through realistic production.

• Cartoon and Animation Studies
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• s.6
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• pp.193-220
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• 2002

The world being made by computers showing great expanses and complex and various expression provides not simply communication places but also a new civilization and a new creative world. Among these, 3D computer graphics, 3D animation and virtual reality technology wore sublimated as a new culture and a new genre of art by joining graphic design and computer engineering. In this study, I tried to make a diagnosis of possibilities, limits and differences of expression in the area of virtual reality computer graphics animation as a comparison between camera action, angle of actual still camera and film camera and virtual software for 3D computer graphics software - Maya, XSI, Softimage3D.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.357-358
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• 2021

Although many people watch the scene with multiple surveillance cameras, it is difficult to ensure that immediate action can be taken in the event of a crime. Therefore, there is a need for a "crime behavior detection system" that can analyze images in real time from multiple surveillance cameras installed in elevators, call immediate crime alerts, and track crime scenes and times effectively. In this paper, a study was conducted to detect violent scenes occurring in elevators using Scene Change Detection. For effective detection, an x2-color histogram combining color histogram and histogram was applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.25-27
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• 2021

With the recent development of deep learning technology, computer vision-based AI technologies have been studied to analyze the abnormal behavior of objects in image information acquired through CCTV cameras. There are many cases where surveillance cameras are installed in dangerous areas or security areas for crime prevention and surveillance. For this reason, companies are conducting studies to determine major situations such as intrusion, roaming, falls, and assault in the surveillance camera environment. In this paper, we propose a real-time abnormal behavior analysis algorithm using object detection and tracking method.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.