• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9A
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• pp.868-873
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• 2010

H.264/AVC video compression standard has been adopted for DMB, digital TV and various next generation broadcasting, communication and consumer electronics applications, and modern DVR system is also based on H.264/AVC standard. Although PC-based DVRs use PCI bus for main interface typically, H.264/AVC codec for SOCs use AHB bus for host interface. In this paper, we present an implementation of PCI to AHB interface module for H.264/AVC codec to efficiently communicate with a PC and experimental results.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.819-822
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• 2014

본 연구에서는 비디오 감시 시스템을 기반으로 한우 축사에서 야간 승가 행위 검출을 위한 최적의 방법을 제안한다. 특히 축사 환경에서는 소들간의 겹침 등 다양한 어려움이 발생하기 때문에, 이를 극복하기 위하여 깊이 정보를 이용하여 승가하는 소의 머리를 자동으로 탐지한다. 즉, 소가 네발로 걸어다니는 통상의 경우 소의 등 높이가 1.3m 정도인데 반해 앞발을 들어 승가하는 경우에는 소의 높이가 1.7m 까지 높아짐에 착안하여, 축사 측면에 설치된 깊이 카메라로부터 소까지의 거리 차이를 이용하면 발정기 탐지를 위한 승가 행위를 자동으로 검출할 수 있음을 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.10
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• pp.67-71
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• 2011

Existing face detection systems, which are used for digital door locks, digital cameras, video surveillance systems, and so on, are software-based implementation for relatively low level resolution images. Therefore, in this case, there are difficulties in detecting faces in a real-time fashion due to the increasing amount of operational processing as well as in allowing the requirements of face detections for HD(High Definition) resolutions. A hardware approach is necessary to efficiently find faces for HD images in real-time embedded systems. This paper proposes and implements a hardware architecture for an LBP(Local Binary Pattern) operation which is a time-consuming part as one of preprocessing steps for face detection. The hardware architecture proposed in this research has been implemented and tested with a FPGA(Field Programmable Gate Array) chip, and shown that the approach guarantees efficient face detection for HD images.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.159-166
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• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.2
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• pp.29-38
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• 2021

The Internet including mobile networks is developing to overcoming the limitation of physical distance and providing or acquiring information from remote locations. However, the systems that use video as primary information require higher bandwidth for recognizing the situation in remote places more accurately through high-quality video as well as lower latency for faster interaction between devices and users. The emergence of the 5th generation mobile network provides features such as high bandwidth and precise location recognition that were not experienced in previous-generation technologies. In addition, the Mobile Edge Computing that minimizes network latency in the mobile network requires a change in the traditional system architecture that was composed of the existing smart device and high availability server system. However, even with 5G and MEC, since there is a limit to overcome the mobile network state fluctuations only by enhancing the network infrastructure, this study proposes a high-definition video streaming system in ultra-low latency based on the SRT protocol that provides Forward Error Correction and Fast Retransmission. The proposed system shows how to deploy software components that are developed in consideration of the nature of 5G and MEC to achieve sub-1 second latency for 4K real-time video streaming. In the last of this paper, we analyze the most significant factor in the entire video transmission process to achieve the lowest possible latency.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.598-605
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• 2018

Behavior awareness is a technology that recognizes human behavior through data and can be used in applications such as risk behavior through video surveillance systems. Conventional behavior recognition algorithms have been performed using the 2D camera image device or multi-mode sensor or multi-view or 3D equipment. When two-dimensional data was used, the recognition rate was low in the behavior recognition of the three-dimensional space, and other methods were difficult due to the complicated equipment configuration and the expensive additional equipment. In this paper, we propose a method of recognizing human behavior using only CCTV images without additional equipment using only RGB and depth information. First, the skeleton extraction algorithm is applied to extract points of joints and body parts. We apply the equations to transform the vector including the displacement vector and the relational vector, and study the continuous vector data through the RNN model. As a result of applying the learned model to various data sets and confirming the accuracy of the behavior recognition, the performance similar to that of the existing algorithm using the 3D information can be verified only by the 2D information.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.155-162
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• 2020

Recently, with the development of computer vision and deep learning technology, research on human action recognition has been actively conducted for video analysis, video surveillance, interactive multimedia, and human machine interaction applications. Diverse techniques have been introduced for human action understanding and classification by many researchers using RGB image, depth image, skeleton and inertial data. However, skeleton-based action discrimination is still a challenging research topic for human machine-interaction. In this paper, we propose an end-to-end skeleton joints mapping of action for generating spatio-temporal image so-called dynamic image. Then, an efficient deep convolution neural network is devised to perform the classification among the action classes. We use publicly accessible UTD-MHAD skeleton dataset for evaluating the performance of the proposed method. As a result of the experiment, the proposed system shows better performance than the existing methods with high accuracy of 97.45%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.461-466
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• 2016

In this paper, a capsule body shaped surveillance/monitoring device is developed. The device includes a camera and GPS module to transmit live video data and real time GPS coordinates respectively using the Intel Edison module. A control application is developed for the smart phones and tablets to wirelessly view the live video stream and location of the capsule device and also to switch between the multiple capsule devices installed at different locations. The coordination between the developed device and the smart phone / tablet is done using the wireless function of the Intel Edison module.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.147-153
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• 2014

In this paper, we present a non-dyadic lens distortion correction model and image restoration method based on local self-similarity to remove jagging and blurring artifacts in the peripheral region of the geometrically corrected image. The proposed method can be applied in various application areas including vehicle real-view cameras, visual surveillance systems, and medical imaging systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.22-29
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• 2010

A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.