• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.3
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• pp.143-150
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• 2008

In this paper, we investigate a real-time environment recognition system based on stereo vision for moving object. This system consists of stereo matching, obstacle detection and distance estimation. In stereo matching part, depth maps can be obtained real road images captured adjustable baseline stereo vision system using belief propagation(BP) algorithm. In detection part, various obstacles are detected using only depth map in case of both v-disparity and column detection method under the real road environment. Finally in estimation part, asymmetric parabola fitting with NCC method improves estimation of obstacle detection. This stereo vision system can be applied to many applications such as unmanned vehicle and robot.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.41-47
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• 2012

MANET has vulnerable structure on security owing to structural characteristics as follows. MANET consisted of moving nodes is that every nodes have to perform function of router. Every node has to provide reliable routing service in cooperation each other. These properties are caused by expose to various attacks. But, it is difficult that position of environment intrusion detection system is established, information is collected, and particularly attack is detected because of moving of nodes in MANET environment. It is not easy that important profile is constructed also. In this paper, conformal predictor - support vector machine(CP-SVM) based intrusion detection technique was proposed in order to do more accurate and efficient intrusion detection. In this study, IDS-agents calculate p value from collected packet and transmit to cluster head, and then other all cluster head have same value and detect abnormal behavior using the value. Cluster form of hierarchical structure was used to reduce consumption of nodes also. Effectiveness of proposed method was confirmed through experiment.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.369-371
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• 2012

Shadow detection and elimination is a critical issue in vision-based system to improve the detection performance of moving objects. However, traditional algorithms are useless at night time because they used the chromaticity and brightness information from the color image sequence. To obtain the high detection performance, we can use the thermal camera and there are shadows by the heat not the light. We proposed a novel algorithm to detect and eliminate the shadows using the thermal intensity and the locality property. By combining two results of the intensity-based and locality-based, we can detect the shadows by the heat and improve the detection performance of moving object.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.121-128
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• 2015

In this paper, we proposed and implemented the effective automatic foreground motion detection algorithm that detect the foreground motion by analyzing the digital video data that captured by the network camera. We classified the background as moving background, fixed background and normal background based on the standard deviation of background and used it to detect the foreground motion. According to the result of experiment, our algorithm decreased the fault detection of the moving background and increased the accuracy of the foreground motion detection. Also it could extract foreground more exactly by using the statistic information of background in the phase of our foreground extraction.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1218-1230
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• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.98.6-98
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• 2002

1. Introduction 2. Detection of the moving object 3. Correspondence problem 4. Experiment 5. Conclusions. References

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.5
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• pp.21-27
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• 2008

Despite recent studies on development of pulse diagnosis systems and needs for commercializing them, the reproducibility is one of the most controversial issues as ever. Because the pulse pressure value, which is one of the important parameters to evaluate reproducibility, is very vulnerable to moving artifacts, the reproducibility can not be obtained easily. In this paper, we suggested a moving artefacts detection system for a pulse diagnosis system so that a pulse diagnosis system can be robust to theses kinds of artefacts by excluding the contaminated parts from the pulse wave signal to be analyzed. This moving artifacts detection system was designed to consist of a three-axis accelerometer, an electromyography amplifier and a two-axis tilt sensor. To assess the suitability of the system, we examined the characteristics of each sensor's output signals with regard to the three specific motions such as extension, flexion and rotation. And, we also examined the each sensor's response to the high-frequency and low-frequency moving artifacts while the pulse wave signal was acquired from a pressure sensor for the pulse diagnosis. From these results, we could find that the response to subject's motions would be reflected in electromyography signal first, in accelerometer signals and in tilt sensor sequently. And, the facts that a stable pulse wave can be acquired in two seconds after high frequency or low frequency motions ended, were also found. Consequently, based on these findings, we set up some rules on the moving artifacts detection and designed an algorithm which is fit for our moving artifacts detection system.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.21-29
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• 2015

This paper proposes a shot change detection algorithm by using frame segmentation and the object changes among moving blocks. In order to detect the rapid moving changes of objects between two consecutive frames, the moving blocks on the diagonal are defined, and their histograms are calculated. When a block of the current frame is compared to the moving blocks of the next frame, the block histograms are used and the threshold of a shot change detection is automatically adjusted by Otsu's threshold method. The proposed algorithm was tested for the various types of color or gray videos such as films, dramas, animations, and video tapes in National Archives of Korea. The experimental results showed that the proposed algorithm could enhance the detection rate when compared to the studied methods that use brightness, histogram, or segmentation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.450-460
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• 2017

T wave is cardiac parameters that represent ventricular repolarization, it is very important to diagnose arrhythmia. Several methods for detecting T wave have been proposed, such as frequency analysis and non-linear approach. However, detection accuracy is at the lower level. This is because of the overlap of the P wave and T wave depending on the heart condition. We propose T wave detection algorithm based on target area extraction through QRS cancellation and moving average. For this purpose, we detected Q, R, S wave from noise-free ECG(electrocardiogram) signal through the preprocessing method. And then we extracted P, T target area by applying decision rule for four PAC(premature atrial contraction) pattern another arrhythmia through moving average and detected T wave using RT interval and threshold of RR interval. The performance of T wave detection is evaluated by using 48 record of MIT-BIH arrhythmia database. The achieved scores indicate the average detection rate of 95.32%.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.4
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• pp.192-198
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• 2018

Recently, the use of drone has been increasing rapidly in many ways. A drone can capture remote objects efficiently so it is suitable for surveillance and security systems. This paper discusses three methods for detecting moving vehicles using a drone. We compare three target detection methods using a background frame, preceding frames, or moving average frames. They are subtracted from a current frame. After the frame subtraction, morphological filters are applied to increase the detection rate and reduce the false alarm rate. In addition, the false alarm region is removed based on the true size of targets. In the experiments, three moving vehicles were captured by a drone, and the detection rate and the false alarm rate were obtained by three different methods and the results are compared.