• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.215-223
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• 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 the Korea Society of Computer and Information
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• v.22 no.12
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• pp.79-85
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• 2017

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1361-1370
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• 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 pig monitoring environment, segmenting each pig from touching-pigs is still entrenched as a difficult problem. In this paper, we propose a segmentation method for touching-pigs by using concave-points and edge information in a video surveillance system. Especially, we interpret the segmentation problem as a time-series analysis problem in order to identify the concave-points generated by touching-pigs. Based on the experimental results with the videos obtained from a domestic pig farm, we believe that the proposed method can accurately segment the touching-pigs.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.1-10
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• 2023

In this paper, the design and implementation of the long-distance wireless transmission technology of high-definition video using domestic wireless communication technology for the existing wired-based CCTV surveillance system and IP camera system was evaluated for performance. The interworking test between the wireless multi-IP camera transmission terminal device and the wireless communication RF module, and the wireless multi-IP camera-based video transmission system integration interworking test confirm the module interworking suitability during video transmission.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.12
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• pp.141-149
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• 2013

This paper presents a high-speed intrusion detection process for multi-core video surveillance systems. The high-speed intrusion detection was designed to a parallel process. Based on the analysis of the conventional process, a parallel intrusion detection process was proposed so as to be accelerated by utilizing multiple processing cores in contemporary computing systems. The proposed process performs the intrusion detection in a per-frame parallel manner, considering the data dependency between frames. The proposed process was validated by implementing a multi-threaded intrusion detection program. For the system having eight processing cores, the detection speed of the proposed program is higher than that of the conventional one by up to 353.76% in terms of the frame rate.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.953-958
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• 2007

Passenger safety is a primary concern of railway system but, it has been urgent issue that dozens of people are killed every year when they are fallen from train platforms. In this paper, we propose a vision based monitoring system for railway station platform. The system immediately perceives dangerous factors of passengers on the platform by using image processing technology. To monitor almost entire length of the track line in the platform, we use several video cameras. Each camera conducts surveillance its own preset monitoring area whether human or dangerous object was fallen in the area. Moreover, to deal with the accident immediately, the system provides local station, central control room employees and train driver with the video information about the accident situation including alarm message. This paper introduces the system overview and detection process with experimental results. According to the results, we expect the proposed system will play a key role for establishing highly intelligent monitoring system in railway.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.288-291
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• 2011

Video stabilization is an important image enhancement widely used in surveillance system in order to improve recognition performance. Most previous methods calculate inter-frame homography to estimate global motion. These methods are relatively slow and suffer from significant depth variations or multiple moving object. In this paper, we propose a fast and practical approach for video stabilization that selects the most reliable key frame as a reference frame to a current frame. We use optical flow to estimate global motion within an adaptively selected region of interest in static camera environment. Optimal global motion is found by probabilistic voting in the space of optical flow. Experiments show that our method can perform real-time video stabilization validated by stabilized images and remarkable reduction of mean color difference between stabilized frames.

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

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1275-1281
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• 2011

This paper presents synthetic circumstantial judgement system that detects and predicts a fire in subway station. Unlike conventional fire surveillance systems that judge the fire or not through smoke, CO, temperature or variation of temperature, a proposed system discovers a fire more easily or gives the alarm high possibility of fire to operator through recognition of fire levels based on Fuzzy Inference System using by FCM and information of objects from video data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1325-1334
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• 2016

The video incident detection System is a probe for rapid detecting the walker, falling, stopped, backwards, smoke situation in tunnel. Recently, the importance is increases from the downtown double deck tunnel in great depth underground space[1], but the legal basis is weak and the vulnerable situation experimental data. So, In this paper, we introduce a long-term log data analysis information in the tunnenl video incident detection system installed and experimental results in order to verify the feasibility of apply to video incident detection system for the double deck tunnel. It is proposed a few things about derives the problem of existing video incident detection system, improvements and reflect attributes for double deck tunnel. The contents described in this paper will contribute to refine the prototype of video incident detection system will apply to future double deck multi-layer tunnels.