• Journal of Information Processing Systems
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• 제15권2호
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• pp.344-358
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• 2019

Surveillance cameras have installed in many places because security and safety is becoming important in modern society. Through surveillance cameras installed, we can deal with troubles and prevent accidents. However, watching surveillance videos and judging the accidental situations is very labor-intensive. So now, the need for research to analyze surveillance videos is growing. This study proposes an algorithm to track multiple persons using SURF and background subtraction. While the SURF algorithm, as a person-tracking algorithm, is robust to scaling, rotating and different viewpoints, SURF makes tracking errors with sudden changes in videos. To resolve such tracking errors, we combined SURF with a background subtraction algorithm and showed that the proposed approach increased the tracking accuracy. In addition, the background subtraction algorithm can detect persons in videos, and SURF can initialize tracking targets with these detected persons, and thus the proposed algorithm can automatically detect the enter/exit of persons.

• 한국항공우주학회지
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• 제39권9호
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• pp.848-857
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• 2011

영상센서 기반의 충돌회피 시스템을 구성하기 위해서는 수 픽셀 이내의 낮은 신호대잡음비 환경에서 다수의 표적을 탐지할 수 있는 알고리듬이 필요하다. 이처럼 영상 내에서 희미하게 나타나는 잠재적인 표적과 잡음을 구분하기 위한 방법으로서 연속적인 영상 정보를 효율적으로 처리하는 Track-Before-Detect (TBD) 알고리듬이 연구되고 있다. 본 논문에서는 기존의 TBD 알고리듬을 확장하여 다수 표적 탐지 요구조건을 만족시키기 위한 두 가지 방식의 기법을 제시하였다. 첫 번째 방식은 동적 계획법과 K-평균 클러스터링 기법에 기반을 두고 있으며 두 번째 방식은 은닉 마르코프 모델에 Sub-Window 기법을 적용하였다. 제안한 방식의 성능 및 차이점은 수치해석 결과를 통해 분석하였다.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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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.

• 한국기계가공학회지
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• 제18권12호
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• pp.38-44
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• 2019

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.

• Journal of Biosystems Engineering
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• 제31권1호
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• pp.59-64
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• 2006

Application of herbicide for rice cropping is inevitable but notorious for its side effect of environmental pollution. Precision fanning will be one of important tools for the least input and sustainable fanning and could be achieved by implementation of the variable rating technology. If a device to detect weeds in rice field is available, herbicide could be applied only to the places where it is needed by the manner of the variable rating technology. The study was carried out to develop an algorithm of image processing to detect weeds in rice field using a machine vision system of multi-spectral digital images. A series of multi-spectral rice field picture of 560, 680 and 800 nm of center wavelengths were acquired from the 27th day to the 39th day after transplanting in the ineffective tillering stage of a rice growing period. A discrimination model to distinguish pixels of weeds from those of rice plant and weed image was developed. The model was proved as having accuracies of 83.6% and 58.9% for identifying the rice plant and the weed, respectively. The model was used in the algorithm to differentiate weed images from mingled images of rice plant and weed in a frame of rice field picture. The developed algorithm was tested with the acquired rice field pictures and resulted that 82.7%, 11.9% and 5.4% of weeds in the pictures were noted as the correctly detected, the undetected and the misclassified as rice, respectively, and 81.9% and 18.0% of rice plants in the pictures were marked as the correctly detected and the misclassified as weed, respectively.

• Structural Engineering and Mechanics
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• 제71권1호
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• pp.57-63
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• 2019

During the last two decades, much joint research regarding vibration based methods has been done, leading to developing various algorithms and techniques. These algorithms and techniques can be divided into modal methods and signal methods. Although modal methods have been widely used for health monitoring and damage detection, signal methods due to higher efficiency have received considerable attention in various fields, including aerospace, mechanical and civil engineering. Signal-based methods are derived directly from the recorded responses through signal processing algorithms to detect damage. According to different signal processing techniques, signal-based methods can be divided into three categories including time domain methods, frequency domain methods, and time-frequency domain methods. The frequency domain methods are well-known and interest in using them has increased in recent years. To determine dynamic behaviours, to identify systems and to detect damages of bridges, different methods and algorithms have been proposed by researchers. In this study, a new algorithm to detect seismic damage in the bridge's piers is suggested. To evaluate the algorithm, an analytical model of a bridge with simple spans is used. Based on the algorithm, before and after damage, the bridge is excited by a sine force, and the piers' responses are measured. The dynamic specifications of the bridge are extracted by Power Spectral Density function. In addition, the Least Square Method is used to detect damage in the bridge's piers. The results indicate that the proposed algorithm can identify the seismic damage effectively. The algorithm is output-only method and measuring the excitation force is not needed. Moreover, the proposed approach does not need numerical models.

• Structural Engineering and Mechanics
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• 제19권1호
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• pp.21-42
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• 2005

A method is presented to detect and assess the structural damage from changes in natural frequencies using Genetic Algorithm (GA). Using the natural frequencies of the structure, it is possible to formulate the inverse problem in optimization terms and then to utilize a solution procedure employing GA to assess the damages. The technique has been applied to a cantilever beam and a plane frame, each one with different damage scenario to study the efficiency of the developed algorithm. A laboratory tested data has been used to verify the proposed algorithm. The study indicates the potentiality of the developed code to solve a wide range of inverse identification problems in a systematic way. The outcomes show that this method can detect and estimate the amount of damages with satisfactory precision.

• 전자공학회논문지S
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• 제34S권11호
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• pp.126-137
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• 1997

This paper describes the algorithm which can estimate road following direction and deetect obstacle using a monocular vision system. This algorithm can estimate the course of vehicle using the vanishing point properties and detect obstacle by statistical method. The proposed algorithm is composed of four steps, which are lane prediction, lane extraction, road following parameter estimation and obstacle detection. It is designed for high processing speed and high accuracy. The former is achieved by a small area named sub-windown in lane existence area, the later is realized by using connected edge points of lane. We would like to present that the new mehod can detect obstacle using the simple statistical method. The paracticalities of the processing speed, the accuracy of the algorithm and proposing obstacle detection method, have been justified through the experiment applied VTR image of the real road to the algorithm.

• 전자공학회논문지A
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• 제30A권1호
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• pp.64-72
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• 1993

본 논문에서는 RAM에서의 functional 고장인 PSF를 검출할수 있는 "알고리듬 마"를 제안한다. 이 알고리듬은 PSF의 형태를 한정시켜서 제한된 범위의 PSF(restricted PSF or neighborhood PSF)를 검출하는 것으로써 "알고리듬 마"는 SNPSF, PNPSF 및 일부의 ANPSF를 검출하며, 고전적인 고장인 stuck-at 고장 및 천이(transition)고장도 검출한다. 이 알고리듬의 시간 복잡도는 1536xP로써 P는 메모리블럭의 분할갯수를 나타낸다. 또한 "알고리듬 마"의 BIST scheme을 제안하였다.

• 설비공학논문집
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• 제15권3호
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• pp.159-165
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• 2003

The fault detection and diagnosis technology may be applied in order to decrease the energy consumption and the maintenance cost of the air-conditioning system. To detect partial faults of the air-conditioning system, a neural network algorithm may be used. In this study, the neural network algorithm using normalized input data by the standard deviation was applied. And the [7$\times$10$\times$10$\times$1] neural network structure was selected. Test results showed that the neural network algorithm using normalized input data was very effective to detect the condenser fouling and the evaporator fan fault of an air-conditioning system.