• 제어로봇시스템학회논문지
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• 제17권1호
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• pp.17-21
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• 2011

This paper presents a real- time detection of on-road succeeding vehicles based on low level edge features and a boosted cascade of Haar-like features. At first, the candidate vehicle location in an image is found by low level horizontal edge and symmetry characteristic of vehicle. Then a boosted cascade of the Haar-like features is applied to the initial hypothesized vehicle location to extract the refined vehicle location. The initial hypothesis generation using simple edge features speeds up the whole detection process and the application of a trained cascade on the hypothesized location increases the accuracy of the detection process. Experimental results on real world road scenario with processing speed of up to 27 frames per second for $720{\times}480$ pixel images are presented.

• Journal of information and communication convergence engineering
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• 제15권3호
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• pp.187-192
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• 2017

Autonomous driving vehicle research demands complex road and lane understanding such as lane departure warning, adaptive cruise control, lane keeping and centering, lane change and turn assist, and driving under complex road conditions. A fast and robust road lane detection subsystem is a basic but important building block for this type of research. In this paper, we propose a method that performs road lane detection from black box input. The proposed system applies Random Sample Consensus to find the best model of road lanes passing through divided regions of the input image under HSV color model. HSV color model is chosen since it explicitly separates chromaticity and luminosity and the narrower hue distribution greatly assists in later segmentation of the frames by limiting color saturation. The implemented method was successful in lane detection on real world on-board testing, exhibiting 86.21% accuracy with 4.3% standard deviation in real time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3475-3489
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• 2014

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

• 미생물학회지
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• 제47권1호
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• pp.87-91
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• 2011

본 연구에서는 분말 식품에서 real-time PCR과 배지배양법을 사용하여 Cronobacter spp.를 검출하는 방법이 비교검증 되었다. 조제분유, 이유식, 미숫가루에 Cronobacter를 인위적으로 접종시킨 후, 식품공전의 방법에 따라 멸균증류수와 Enterobacteriaceae enrichment (EE) broth에서 각각 1, 2차 증균배양 하였으며, Druggan-Forsythe-Iversen에 선택배양하여 Cronobacter를 검출하였다. Real-time PCR은 멸균증류수 및 EE broth에서 1 ml을 채취한 후 DNA를 추출하여 시행하였다. 실험결과 모든 식품에서 배지배양법과 real-time PCR간에는 통계학적 유의차가 존재하지 않았다(p>0.05). 한편 모든 실험회차에서 real-time PCR 수행 시, 1차 증균액인 멸균증류수에서의 양성검출율이 2차 증균액인 EE broth에서보다 높았는데, 이는 2차 증균액 내의 구성성분 중 일부분이 real-time PCR의 반응을 저해했기 때문으로 사료된다. 연구결과를 종합해 볼 때, 1차 증균 후, real-time PCR을 통해 Cronobacter를 검출하는 방법은 정확한 민감도를 보이면서도 시간과 노동력을 절감할 수 있는 효과적인 방법으로 사료된다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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• pp.908-911
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• 2004

In this paper, we describe a robust image processing algorithm to recognize the road lane in real-time. For the real-time processing, a detection area is decided by a lane segment of a previous frame and edges are detected on the basis of the lane width. For the robust driving, the global threshold with the Otsu algorithm is used to get a binary image in a frame. Therefore, reliable edges are obtained from the algorithms suggested in this paper in a short time. Lastly, the lane segment is found by hough transform. We made a RC(Radio Control) car equipped with a vision system and verified these algorithms using the RC Car.

• 한국IT서비스학회지
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• 제11권sup호
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• pp.113-121
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• 2012

In this study, a real-time fall detection system based on a smartphone equipped with three-axis accelerometer and magnetometer was proposed and evaluated. The proposed system provides a service that detects falls in real time, triggers alarm sound, and sends emergency SMS(Short Message Service) if the alarm is not deactivated within a predefined time. When both of the acceleration magnitude and angle displacement of the smartphone attached to waist belt are greater than predefined thresholds, it is detected as a fall. The proposed system was evaluated against activities of daily living(walking, jogging, sitting down, standing up, ascending stairs, and descending stairs) and unintended falls induced by a proprietary pneumatic-powered mattress. With the thresholds of acceleration magnitude 1.7g and angle displacement $80^{\circ}$, it showed 96.5% accuracy to detect the falls while all the activities of daily living were not detected as fall.

• 정보와 통신
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• 제10권10호
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• pp.22-29
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• 1993

This paper describes development of a real-time voice recognition dialing system which can recognize around one hundred word vocabularies in speaker independent mode. The voice recognition algorithm is implemented on a DSP board with a telephone interface plugged in an IBM PC AT/486. In the DSP board, procedures for feature extraction, vector quantization(VQ), and end-point detection are performed simultaneously in every 10msec frame interval to satisfy real-time constraints after the word starting point detection. In addition, we optimize the VQ codebook size and the end-point detection procedure to reduce recognition time and memory requirement. The demonstration system is being displayed in MOBILAB of Korea Mobile Telecom at the Taejon EXPO '93.

• 대한수의학회지
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• 제53권1호
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• pp.25-28
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• 2013

The aim of this study was to applicate and evaluate a SYBR Green real-time PCR for the specific detection of Salmonella spp. Specificity of the PCR method was confirmed with 48 Salmonella spp. and 5 non-Salmonella strains using invA gene primer. The average threshold cycle ($C_T$) of Salmonella spp. was $11.83{\pm}0.78$ while non-Salmonella spp. was $30.86{\pm}1.19$. Correlation coefficients of standard curves constructed using $C_T$ versus copy number of Salmonella Enteritidis ATCC 13076 showed good linearity ($R^2=0.993$; slope = 3.563). Minimum level of detection with the method was > $10^2$ colony forming units (CFU)/mL. These results suggested that the SYBR Green real-time PCR might be applicable for the specific detection of Salmonella spp. isolates.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.4120-4132
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• 2017

In this research, we implement a deformable object simulation system using OpenGL's shader language, GLSL4.3. Deformable object simulation is implemented by using volumetric mass-spring system suitable for real-time simulation among the methods of deformable object simulation. The compute shader in GLSL 4.3 which helps to access the GPU resources, is used to parallelize the operations of existing deformable object simulation systems. The proposed system is implemented using a compute shader for parallel processing and it includes a bounding box-based collision detection solution. In general, the collision detection is one of severe computing bottlenecks in simulation of multiple deformable objects. In order to validate an efficiency of the system, we performed the experiments using the 3D volumetric objects. We compared the performance of multiple deformable object simulations between CPU and GPU to analyze the effectiveness of parallel processing using GLSL. Moreover, we measured the computation time of bounding box-based collision detection to show that collision detection can be processed in real-time. The experiments using 3D volumetric models with 10K faces showed the GPU-based parallel simulation improves performance by 98% over the CPU-based simulation, and the overall steps including collision detection and rendering could be processed in real-time frame rate of 218.11 FPS.