• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.248-256
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• 2007

Recently, the network Infrastructure grows rapidly and the digital image compression technique has made remarkable progress. Therefore, the demand of the real-time image surveillance system which uses a network camera server has been increasing. Network Camera Server has emerged as an attractive alternative to the CCTV for the wireless video surveillance. In this article, the model of JPEG Streamer for collecting and delivering JPEG image is designed and realized as a key module for the wireless video surveillance system. The thread pool and shared memory have been used to improve the stability and efficiency of the JPEG Streamer. In addition, the concept of double buffering is of much benefit to improve the quality of real-time image. In this article, the wireless video surveillance system by using JPEG Streamer is suggested to send the real-time image through the wireless internet with the personal digital assistance (PDA).

• International conference on construction engineering and project management
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• 2015.10a
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• pp.336-338
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• 2015

This research is to propose an applicable framework for real-time health surveillance and safety monitoring at construction sites. First this study aims at finding (1) a framework for health surveillance that is likely to benefit employers and employees in the industry, (2) a valid way to identify factors or conditions with potential health concerns that can occur under particular work conditions, (3) An effective way to apply wireless/mobile sensors to construction workers using real-time/live data transmission methods, and (4) A relationship between a worker's vital signs and job site environment. Biosensors for physiological response and devices for weather/work related data are to collect real-time data. Relationships between jobs and physiological responses are analyzed and factors that touched particularly contributing to certain responses are identified. When data are incorporated with tasks, factors affecting tasks can be identified to estimate the magnitude of the factors. By comparing work and normal responses possible precautionary actions can be considered. In addition, the study would be lead to improving (1) trade-specific dynamic work schedules for workers which would be based on various factors affecting worker health level and (2) reevaluating worker productivity with health status and work schedule, thereby seeking ways to maximize worker productivity. Through a study, the paper presents expected benefits of implementing health monitoring.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.8-19
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• 2023

This paper presents advancement in multi- unmanned aerial vehicle (UAV) cooperative area surveillance, focusing on optimizing UAV route planning through the application of genetic algorithms. Addressing the complexities of comprehensive coverage, two real-time dynamic path planning methods are introduced, leveraging genetic algorithms to enhance surveillance efficiency while accounting for flight constraints. These methodologies adapt multi-UAV routes by encoding turning angles and employing coverage-driven fitness functions, facilitating real-time monitoring optimization. The paper introduces a novel path planning model for scenarios where UAVs navigate collaboratively without predetermined destinations during regional surveillance. Empirical evaluations confirm the effectiveness of the proposed methods, showcasing improved coverage and heightened efficiency in multi-UAV path planning. Furthermore, we introduce innovative optimization strategies, (Foresightedness and Multi-step) offering distinct trade-offs between solution quality and computational time. This research contributes innovative solutions to the intricate challenges of cooperative area surveillance, showcasing the transformative potential of genetic algorithms in multi-UAV technology. By enabling smarter route planning, these methods underscore the feasibility of more efficient, adaptable, and intelligent cooperative surveillance missions.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4077-4082
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• 2023

Large areas must be rapidly screened to monitor radiation in marine environments. For this purpose, this study developed a mobile real-time gamma-ray measurement system for shipboard use and evaluated its performance. The system was developed to measure engine or generator cooling water by installing a canister inside the ship. The minimum detectable activity of the system is about 0.8 Bq/L for a 60 s measurement period, and real-time data transmission and remote control are possible. The system was tested in the field and is currently being installed and operated on ships in service. Such a ship-based real-time gamma-radiation measurement system is suitable for a wide range of marine radiation surveillance applications and is expected to be rapidly deployed.

• Smart Media Journal
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• v.7 no.2
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• pp.40-46
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• 2018

The conventional CCTV surveillance system for preventing accidents and incidents misses 95% of the data after 22 minutes where one person monitors multiple CCTV. To address this issue, researchers have studied the computer-based intelligent video surveillance system for notifying people of the abnormal situation. However, because the system is involved in the problems of power consumption and costs, the intelligent video surveillance system based on embedded modules has been studied. This paper implements the intelligent video surveillance system based on embedded modules for detecting intruders, detecting fires and detecting loitering, falling. Moreover, the algorithm and the embedded module optimization method are applied to implement real-time processing. The intelligent video surveillance system based on embedded modules is implemented in Raspberry Pi. The algorithm processing time is 0.95 seconds on Raspberry Pi before optimization, and 0.47 seconds on Raspberry Pi after optimization, reduced processing time by 50.52%. Therefore, this suggests real processing possibility of the intelligent video surveillance system based on the embedded modules is possible.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.23-29
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• 2009

Blob detection is an essential ingredient process in some computer applications such as intelligent visual surveillance. However, previous blob detection algorithms are still computationally heavy so that supporting real-time multi-channel intelligent visual surveillance in a workstation or even one-channel real-time visual surveillance in a embedded system using them turns out prohibitively difficult. In this paper, we propose a fast and precise blob detection algorithm for visual surveillance. Blob detection in visual surveillance goes through several processing steps: foreground mask extraction, foreground mask correction, and connected component labeling. Foreground mask correction necessary for a precise detection is usually accomplished using morphological operations like opening and closing. Morphological operations are computationally expensive and moreover, they are difficult to run in parallel with connected component labeling routine since they need much different processing from what connected component labeling does. In this paper, we first develop a fast and precise foreground mask correction method utilizing on neighbor pixel checking which is also employed in connected component labeling so that the developed foreground mask correction method can be incorporated into connected component labeling routine. Through experiments, it is verified that our proposed blob detection algorithm based on the foreground mask correction method developed in this paper shows better processing speed and more precise blob detection.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1345-1360
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• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• Journal of IKEEE
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• v.18 no.4
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• pp.612-619
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• 2014

This paper proposes a real-time multi-camera video surveillance system with automatic resolution control using motion detection. In ordinary times, it acquires 4 channels of QVGA images, and it merges them into single VGA image and transmit it. When motion is detected, it automatically increases the resolution of motion-occurring channel to VGA and decreases those of 3 other channels to QQVGA, and then these images are overlaid and transmitted. Thus, it can magnifies and watches the motion-occurring channel while maintaining transmission bandwidth and monitoring all other channels. When it is synthesized with 0.18 um technology, the maximum operating frequency is 110 MHz, which can theoretically support 4 HD cameras.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.311-316
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• 2009

This paper presents the motion estimation algorithm on real-time for mobile surveillance robot using particle filter. the particle filter that based on the monte carlo's sampling method, use bayesian conditional probability model which having prior distribution probability and posterior distribution probability. However, the initial probability density was set to define randomly in the most of particle filter. In this paper, we find first the initial probability density using Sum of Absolute Difference(SAD). and we applied it in the partical filter. In result, more robust real-time estimation and tracking system on the randomly moving object was realized in the mobile surveillance robot environments.