• Journal of Korea Multimedia Society
• /
• v.18 no.9
• /
• pp.1047-1057
• /
• 2015

In this paper, we propose an effective wireless transmission technology, under poor wireless transmission channel surroundings caused by speedy flying, that are able to transmit high quality video recording information and surveillance data via accessing to various wireless networking services architecture such as One-on-One, Many-on-One, One-on-Many, Over the Horizon. The Real-Time Position Analysis(RAPA) method is also suggested to provide more meaningful video information of shooting area. The suggested wireless transmission technology and RAPA can make remote control of UAV's flight route to get valuable topography information. Because of the benefit to get both of video information and GPS data of shooting area simultaneously, the result of study can be applied to various application sphere including UAV that requires high speed wireless transmission.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.10
• /
• pp.3538-3556
• /
• 2014

This paper proposes a novel video stitching method that improves real-time performance and visual quality of a multi-camera video surveillance system. A two-stage seam searching algorithm based on enhanced dynamic programming is proposed. It can obtain satisfactory result and achieve better real-time performance than traditional seam-searching methods. The experiments show that the computing time is reduced by 66.4% using the proposed algorithm compared with enhanced dynamic programming, while the seam-searching accuracy is maintained. A real-time local update scheme reduces the deformation effect caused by moving objects passing through the seam, and a seam-based local color transfer model is constructed and applied to achieve smooth transition in the overlapped area, and overcome the traditional pixel blending methods. The effectiveness of the proposed method is proved in the experiements.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.11 no.3
• /
• pp.63-69
• /
• 2012

In this paper, we introduced design of intelligent surveillance camera system and typical event processing scenario for urban transit. To analyze video, we studied events that frequently occur in surveillance camera system. Event processing scenario is designed for seven representative situations(designated area intrusion, object abandon, object removal in designated area, object tracking, loitering and congestion measurement) in urban transit. Our system is optimized for low hardware complexity, real time processing and scenario dependent solution.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.62 no.4
• /
• pp.164-168
• /
• 2013

Recently, in the elevator of the buildings, such as apartment and office building, the visitor monitoring cameras for surveillance, advertising LCD to provide a variety of information, emergency call devices, and safe driving information sensors are installed. A variety of multimedia data from these devices to the central control office, management office, or to guard room are transmitted in real-time. Each sub-systems in the elevator are installed with a separate lines and operated independently and use different management principals, so integrated management for each sub-systems are very difficult. In this study, we develop a new IP-based multi-channel video surveillance system which is integrated surveillance camera, emergency call devices, communications devices, various sensors in the elevator, DVR, ODM, and can manage all devices by two-way communication and integrated interface. And we evaluated the performance of the developed system.

• Journal of Digital Contents Society
• /
• v.18 no.6
• /
• pp.1009-1015
• /
• 2017

Recently, video surveillance is becoming more and more common as many camera are installed due to crime, terrorism, traffic and security. And systems that control cameras are becoming increasingly general. Video input from the installed camera is monitored by the multiscreen at the central control center, it is essential to simultaneously monitor multiscreen in real-time to quickly respond to situations or dangers. However, monitoring of multiscreen in a mobile environment such as a smart phone is not applied to hardware specifications or network bandwidth problems. For resolving these problems, in this paper, we propose a system that can monitor multiscreen in real-time in mobile-phone environment. We reconstruct the desired multiscreen through transcoding, it is possible to monitor continuously video streaming of multiple cameras, and to have the advantage of being mobile in mobile-phone environment.

• Journal of Advanced Navigation Technology
• /
• v.15 no.6
• /
• pp.1234-1240
• /
• 2011

Recently lots of problems are emerged on the conventional surveillance systems at several areas. Many research activities have been processing on those problems. Therefore, in this paper, it is helpful to all sorts of accident prevention and safe driving, and risks linked to the outside or the administrator tells, that intelligent video surveillance system which can be real-time analysis and monitoring configuration, technical elements, required features, application and its applies.

• Journal of Korea Multimedia Society
• /
• v.25 no.6
• /
• pp.807-818
• /
• 2022

In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.20 no.3
• /
• pp.113-120
• /
• 2010

Content security and privacy protection are important issues in emerging network-based video surveillance applications. Especially, satisfying both real-time constraint and energy efficiency with embedded system-based video sensors is challenging since the battery-operated sensors need to compress and protect video content in real-time. In this paper, we propose a multicore-based solution to compress and protect video surveillance data, and evaluate the effectiveness of the solution in terms of both real-time constraint and energy efficiency. Based on the experimental results with MPEG2/AES software, we confirm that the multicore-based solution can improve the energy efficiency of a singlecore-based solution by a factor of 30 under the real-time constraint.

• Journal of Korea Multimedia Society
• /
• v.19 no.2
• /
• pp.215-223
• /
• 2016

To reduce huge losses in pig farms, weaning pigs with weak immune systems are required to be carefully supervised. Even if various researches have been performed for livestock monitoring environment, segmenting each pig from touching pigs is still entrenched as a difficult problem. In this paper, we propose a real-time segmentation method for moving pigs by using motion information in a 24-h video surveillance system. The experimental results with the videos obtained from a domestic pig farm illustrated the possibility for segmenting by using our proposed method in real-time.

• Journal of Internet Computing and Services
• /
• v.15 no.3
• /
• pp.45-52
• /
• 2014

The Ubiquitous-City (U-City) is a smart or intelligent city to satisfy human beings' desire to enjoy IT services with any device, anytime, anywhere. It is a future city model based on Internet of everything or things (IoE or IoT). It includes a lot of video cameras which are networked together. The networked video cameras support a lot of U-City services as one of the main input data together with sensors. They generate huge amount of video information, real big data for the U-City all the time. It is usually required that the U-City manipulates the big data in real-time. And it is not easy at all. Also, many times, it is required that the accumulated video data are analyzed to detect an event or find a figure among them. It requires a lot of computational power and usually takes a lot of time. Currently we can find researches which try to reduce the processing time of the big video data. Cloud computing can be a good solution to address this matter. There are many cloud computing methodologies which can be used to address the matter. MapReduce is an interesting and attractive methodology for it. It has many advantages and is getting popularity in many areas. Video cameras evolve day by day so that the resolution improves sharply. It leads to the exponential growth of the produced data by the networked video cameras. We are coping with real big data when we have to deal with video image data which are produced by the good quality video cameras. A video surveillance system was not useful until we find the cloud computing. But it is now being widely spread in U-Cities since we find some useful methodologies. Video data are unstructured data thus it is not easy to find a good research result of analyzing the data with MapReduce. This paper presents an analyzing system for the video surveillance system, which is a cloud-computing based video data management system. It is easy to deploy, flexible and reliable. It consists of the video manager, the video monitors, the storage for the video images, the storage client and streaming IN component. The "video monitor" for the video images consists of "video translater" and "protocol manager". The "storage" contains MapReduce analyzer. All components were designed according to the functional requirement of video surveillance system. The "streaming IN" component receives the video data from the networked video cameras and delivers them to the "storage client". It also manages the bottleneck of the network to smooth the data stream. The "storage client" receives the video data from the "streaming IN" component and stores them to the storage. It also helps other components to access the storage. The "video monitor" component transfers the video data by smoothly streaming and manages the protocol. The "video translator" sub-component enables users to manage the resolution, the codec and the frame rate of the video image. The "protocol" sub-component manages the Real Time Streaming Protocol (RTSP) and Real Time Messaging Protocol (RTMP). We use Hadoop Distributed File System(HDFS) for the storage of cloud computing. Hadoop stores the data in HDFS and provides the platform that can process data with simple MapReduce programming model. We suggest our own methodology to analyze the video images using MapReduce in this paper. That is, the workflow of video analysis is presented and detailed explanation is given in this paper. The performance evaluation was experiment and we found that our proposed system worked well. The performance evaluation results are presented in this paper with analysis. With our cluster system, we used compressed $1920{\times}1080(FHD)$ resolution video data, H.264 codec and HDFS as video storage. We measured the processing time according to the number of frame per mapper. Tracing the optimal splitting size of input data and the processing time according to the number of node, we found the linearity of the system performance.