• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.165-168
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• 2019

The advancement in technology has led to the enormous use of multimedia applications. The video/image recorded by such applications provides critical information that can be used as a visual evidence. However, owing to the adequacy in using different editing tools, it is susceptible to malicious alterations. Thus, the reliability or the integrity of the visual information should be verified before using it as an evidence. In this paper, we propose an integrity verification method for the surveillance system using randomized hashing. The integrity value of the surveillance data is generated using the randomized hashing and elliptic curve cryptography (ECC), which is used later for the validation. The experimental results obtained from the embedded accident data recorder (ADR) system shows that the proposed method is very efficient and provides a high level of security.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.866-871
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• 2009

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. Scenario is designed for estimation in the case of seven representative situations(designated area invasion, an object left alone, removed object 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 Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.105-116
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• 2019

Most of the public and private buildings in Korea are installing CCTV for crime prevention and follow-up action, insider security, facility safety, and fire prevention, and the number of installations is increasing each year. In the questionnaire conducted on the increasing CCTV, many reactions were positive in terms of the prevention of crime that could occur due to the installation, rather than negative views such as privacy violation caused by CCTV shooting. However, CCTV poses a lot of privacy risks, and when the image data is collected using the cloud, the personal information of the subject can be leaked. InseCam relayed the CCTV surveillance video of each country in real time, including the front camera of the notebook computer, which caused a big issue. In this paper, we introduce a system to prevent leakage of private information and enhance the security of the cloud system by processing the privacy technique on image information about a subject photographed through CCTV.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.574-578
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• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• Annual Conference of KIPS
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• 2009.04a
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• pp.35-38
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• 2009

The paper presents an effective method to detect fire in video surveillance and monitoring system. The main contribution of this work is that we successfully use the Hidden Markov Models in the process of detecting the fire with a few preprocessing steps. First, the moving pixels detected from image difference, the color values obtained from the fire flames, and their pixels clustering are applied to obtain the image regions labeled as fire candidates; secondly, utilizing massive training data, including fire videos and non-fire videos, creates the Hidden Markov Models of fire and non-fire, which are used to make the final decision that whether the frame of the real-time video has fire or not in both temporal and spatial analysis. Experimental results demonstrate that it is not only robust but also has a very low false alarm rate, furthermore, on the ground that the HMM training which takes up the most time of our whole procedure is off-line calculated, the real-time detection and alarm can be well implemented when compared with the other existing methods.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Spatial Information Research
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• v.21 no.3
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• pp.11-19
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• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.2
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• pp.51-58
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• 2017

Because of safety and security, Surveillance camera market is growing. Accordingly, Study on video recognition and tracking is also actively in progress, but There is a limit to identify object by obtaining the information of object identified and tracked. Especially, It is more difficult to identify multiple objects in open space like shopping mall, airport and others utilized surveillance camera. Therefore, This paper proposed adding object identification function by using RFID to existing video-based object recognition and tracking system. Also, We tried to complement each other to solve the problem of video and RFID based. Thus, through the interaction of system modules We propose a solution to the problems of failing video-based object recognize and tracking and the problems that could be cased by the recognition error of RFID. The system designed to identify the object by classifying the identification of object in four steps so that the data reliability of the identified object can be maintained. To judge the efficiency of this system, this demonstrated by implementing the simulation program.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.37-43
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• 2015

In this paper, a new background subtraction algorithm for video based fence monitoring surveillance systems is proposed. We adopt the sampling based background subtraction technique and focus on the two main issues: handling highly dynamic environment and handling the flickering nature of pulse based IR (infrared) lamp. Natural scenes from fence monitoring system are usually composed of several dynamic entities such as swaying trees, moving water, waves and rain. To deal with such dynamic backgrounds, we utilize the confidence factor for each background value of the input image. For the flickering IR lamp, the original sampling based technique is extended to handle double background models. Experimental results revealed that our method works well in real fence monitoring surveillance systems.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.789-790
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• 2008

In this paper, we introduce the multi-target-based auto surveillance algorithm. Multi-target-based surveillance system detects intrusion objects in the specified areas. The proposed algorithm can divide into two parts: i) background generation, ii) object extraction. In this paper, one of the optical flow equation methods for estimation of gradient method used to generate the background [2]. In addition, the objects and back- ground video images that are continually entering the differential extraction.