• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.855-860
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• 2017

One of the most important issues in Wireless Sensor Networks is to save energy of the sensor node. But transmission latency is also the problem to solve for some applications such as military surveillance, object tracking. In these applications sensor node needs to send lots of data in limited time when an even such as object appearance occurs. So a delay efficient data transmission method is required. In this paper we propose a MAC protocol adequate for those applications. This paper proposed a low delay data transmission mechanism for military surveillance in wireless sensor networks. In the MAC protocol, a receiver node sends another beacon frame to sender node after receiving data packet. Using this second beacon frame, fast hop-to-hop transmission can be performed. Results have shown that the proposed MAC control mechanism outperformed RI-MAC protocol in the terms of latency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.11
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• pp.1104-1113
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• 2014

Future combat systems can be represented as the NCW (Network Centric Warefare), which is based on the concept of Sensor-to-Shooter. A wireless video sensor networking technology, one of the core components of NCW, has been actively applied for the purpose of tactical surveillance. In such a surveillance sensor network, multi-composite sensors, especially consisting of image sensors are utilized to improve reliability for intrusion detection and enemy tracing. However, these sensors may cause a problem of requiring very high network capacity and energy consumption. In order to alleviate this problem, this paper proposes an image data transmission scheme based on resource reservation. The proposed scheme can make it possible to have more reliable image data transmission by choosing proper multiple interfaces, while trying to control resolution and compression quality of image data based on network resource availability. By the performance analysis using NS-3 simulation, we have confirmed the transmission reliability as well as energy efficiency of the proposed scheme.

• Safety and Health at Work
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• v.13 no.1
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• pp.112-116
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• 2022

Background/Aims: The Gachon Regional Occupational Cohort Study (GROCS) is a large-scale longitudinal study of occupational safety and health data (covering Work Environment Monitoring, Workers' Health Surveillance, and Occupational Health Service) conducted by the Gachon University Gil Medical Center (GUGMC) in Incheon, Republic of Korea. We conducted GROCS to identify the health effects of workers' occupational risks, behavior, socioeconomic status, and life style. Methods: The GROCS includes data from Work Environment Monitoring, Workers' Health Surveillance, and Occupational Health Service. The baseline year for all data collection was 2018. Work Environment Monitoring was conducted in 240 companies located in Incheon. General Health Examination and Special Health Examination were performed on 32,725 and 9,504 workers, respectively. Occupational Health Services were provided to 16,883 workers in 171 companies. These data have been collected and operated at an external data management institution and were provided as a retrospective cohort after removing personal identification information. Results: In 2018, the total number of companies was 2,854, among which which 488 special Health Examination, 171 Work Environment Monitoring, and 240 Occupational Health Service. The proportion of companies undergoing Special Health Examination was 17.1%, the proportion of companies undergoing Work Environment Monitoring was 8.4%, and the proportion of Companies undergoing Occupational Health Service was 6.0%. Conclusion: GROCS expects researchers to utilize its useful and reliable resource for occupational health and surveillance with for academic or political purposes to lead to improved workers' health and working environment.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1984-1988
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• 2009

MRT has various surveillance systems for passenger's safety and facility protection which are consisted of fire detection, trespasser observation and so on. However, these systems are not closely related each other because it is designed just for its own purpose, so it could be make wrong decision to surveillance system without important information to determine an accident or disaster. For more accurate event detection, surveillance system needs total situation-aware method using complementary data. This study introduces context-aware system for complex and accurate event detection. Therefore, we apply context-aware system to MRT surveillance system, selecting context-aware parameters and appling them to it.

• Smart Media Journal
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• v.13 no.4
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• pp.65-75
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• 2024

This paper proposes a new approach to video surveillance systems, which have become essential components in modern urban management. By utilizing blockchain and IPFS, it enhances data integrity and privacy protection. Additionally, anomaly detection and automatic video storage are enabled through object detection technology, thus improving urban safety and security. This integrated approach serves as an efficient management methodology for surveillance systems, providing city administrators and citizens with a safer and more effective monitoring environment.

• Knowledge Management Research
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• v.18 no.3
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• pp.63-80
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• 2017

Big Data environment is established by accumulating vast amounts of data as users continuously share and provide personal information in online environment. Accordingly, the more data is accumulated in online environment, the more data is accessible easily by third parties without users' permissions compared to the past. By utilizing strategies based on data-driven, firms recently make it possible to predict customers' preferences and consuming propensity relatively exactly. This Big Data environment, on the other hand, establishes 'Dataveillance' which means anybody can watch or control users' behaviors by using data itself which is stored online. Main objective of this study is to identify the relationship between Dataveillance and users' online privacy protection behaviors. To achieve it, we first investigate perceived online service efficiency; loss of control on privacy; offline surveillance; necessity of regulation influences on users' perceived threats which is generated by Dataveillance.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1421-1426
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• 2003

In this paper, we implement a picture surveillance system using IBM-compatible PC with web camera. The reflex RGB data captured from WebCam is stored to HDD through USB port at every 5 seconds intervals. Also, if strangers are detected through Motion Detection routine, warning voice message is broadcasted and invasion message is transmitted by e-mail and transmit the e-mail title to mobile phone through WAP(Wireless Application Protocol) push. The detected image is stored to hard-disk in ‘month-day-hour-minute-second. jpg’ data type. And the image data is transmitted to web server through FTP(File Transfer Protocol) because invader can deletes or destroys the image data on hard-disk We implement a surveillance system which is able to utilize through internet regardless of time and space.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.169-177
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• 2014

It is essential to support the image and video analysis technology such as motion detection since the DVR and NVR storage were adopted in the real time visual surveillance system. Especially the network camera would be popular as a video input device. The traditional CCTV that supports analog video data get be replaced by the network camera. In this paper, we present the design and implementation of video surveillance system that provides the real time motion detection by the video storage server. The mobile application also has been implemented in order to provides the retrieval functionality of image analysis results. We develop the video analysis server with open source library OpenCV and implement the daemon process for video input processing and real-time image analysis in our video surveillance system.

• Journal of Korea Multimedia Society
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• v.17 no.4
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• pp.420-432
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• 2014

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.