• 한국멀티미디어학회논문지
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• 제11권6호
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• pp.787-795
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• 2008

Human recognition on camera is an interesting topic in computer vision. While fingerprint and face recognition have been become common, gait is considered as a new biometric feature for distance recognition. In this paper, we propose a gait recognition algorithm based on the knee angle, 2 feet distance, walking velocity and head direction of a person who appear in camera view on one gait cycle. The background subtraction method firstly use for binary moving object extraction and then base on it we continue detect the leg region, head region and get gait features (leg angle, leg swing amplitude). Another feature, walking speed, also can be detected after a gait cycle finished. And then, we compute the errors between calculated features and stored features for recognition. This method gives good results when we performed testing using indoor and outdoor landscape in both lateral, oblique view.

• International journal of advanced smart convergence
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• 제7권4호
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• pp.40-49
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• 2018

We propose a more stable robust recognition algorithm which detects faces reliably even in cases where there are changes in lighting and angle of view, as well it satisfies efficiency in calculation and detection performance. We propose detects the face area alone after normalization through pre-processing and obtains a feature vector using (PCA). The feature vector is applied to LDA and using Euclidean distance of intra-class variance and inter class variance in the 2nd dimension, the final analysis and matching is performed. Experimental results show that the proposed method has a wider distribution when the input image is rotated $45^{\circ}$ left / right. We can improve the recognition rate by applying this feature value to a single algorithm and complex algorithm, and it is possible to recognize in real time because it does not require much calculation amount due to dimensional reduction.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2327-2330
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• 2003

This paper deals with face detection and recognition using ellipsodal information and wavelet packet analysis. We proposed two methods. First, Face detection method uses general ellipsodal information of human face contour and we find eye position on wavelet transformed face images A novel method for recognition of views of human faces under roughly constant illumination is presented. Second, The proposed Face recognition scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtered images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, the Euclidian distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the MIT FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. The proposed system achieved an rate of 97％(MIT data), 95.8％(FERET databace)

• 한국정보과학회논문지:소프트웨어및응용
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• 제35권12호
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• pp.753-763
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• 2008

UCC(User Created Contents)의 급격한 증가에 따라 저작권 문제도 크게 대두되고 있다. 자동 로고 인식은 이러한 저작권 문제를 해결하기 위한 효율적인 방법이다. 로고는 다양한 특징을 가지고 있고, 이러한 특징들은 로고 검출과 인식을 어렵게 한다. 특히, 비디오 내에 빈번한 로고 전이가 일어날 경우, 정확한 로고 인식과 로고 기반 분할이 어렵다. 따라서 본 논문에서는 디지털 비디오에서 로고 인식을 위한 정확한 전이 검출 방법과 다양한 로고 타입 인식 방법을 제안한다. 제안한 로고 검출과 로고에 따른 비디오 분할을 이용하여 다양한 비디오에 대한 좋은 실험 결과를 얻을 수 있었다.

• 대한전자공학회논문지SP
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• 제44권6호
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• pp.84-92
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• 2007

본 연구는 원거리에서 걸음걸이 (보행)의 특성을 분석하여 인간을 식별하는 보행인식 (gait recognition) 기술을 다중 특징점 기반으로 확장하여 인식률 및 오류 내성을 향상시키는 기술을 제안한다. 보다 구체적으로 i)움직임 검출, ii) 객체 영역 검출, iii) 머리 영역 검출, 그리고, iv) 능동 형태 모델을 이용하여 기본 알고리듬 (gait baseline algorithm)의 문제점인 전처리 과정없이 그림자 영향과 낮은 인식률을 개선하였다. 제안된 알고리듬은 HumanID Gait Challenge (HGCD) 데이터집합을 이용한 실험을 통해 환경 변화요인에도 강건한 인간 보행인식이 가능함을 확인할 수 있다.

• Smart Structures and Systems
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• 제29권1호
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• 한국컴퓨터정보학회논문지
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• 제10권5호
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• pp.353-362
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• 2005

최근 들어 바이모달 인식에 관한 연구가 활발히 진행되고 있다. 본 논문에서는 음성 정보와 얼굴정보를 이용하여 바이모달 시스템을 구현하였다. 얼굴인식은 얼굴 검출과 얼굴 인식 두 부분으로 나누어서 실험을 하였다. 얼굴 검출 단계에서는 AdaBoost를 이용하여 얼굴 후보 영역을 검출 한 뒤 PCA를 통해 특징 벡터 계수를 줄였다. PCA를 통해 추출된 특징 벡터를 객체 분류 기법인 SVM을 이용하여 얼굴을 검출 및 인식하였다. 음성인식은 MFCC를 이용하여 음성 특징 추출을 하였으며 HMM을 이용하여 음성인식을 하였다. 인식결과, 단일 인식을 사용하는 것보다 얼굴과 음성을 같이 사용하였을 때 인식률의 향상을 가져왔고, 잡음 환경에서는 더욱 높은 성능을 나타냈었다.

• 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.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1802-1811
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• 2016

A novel battery SOH estimation algorithm based on outlier detection has been presented. The Battery state of health (SOH) is one of the most important parameters that describes the usability state of the power battery system. Firstly, a battery system model with lifetime fading characteristic was established, and the battery characteristic parameters were acquired from the lifetime fading process. Then, the outlier detection method based on angular distribution was used to identify the outliers among the battery behaviors. Lastly, the functional relationship between battery SOH and the outlier distribution was obtained by polynomial fitting method. The experimental results show that the algorithm can identify the outliers accurately, and the absolute error between the SOH estimation value and true value is less than 3%.

• 센서학회지
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• 제28권3호
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• pp.157-163
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• 2019

The LiDAR sensor, which is widely regarded as one of the most important sensors, has recently undergone active commercialization owing to the significant growth in the production of ADAS and autonomous vehicle components. The LiDAR sensor technology involves radiating a laser beam at a particular angle and acquiring a three-dimensional image by measuring the lapsed time of the laser beam that has returned after being reflected. The LiDAR sensor has been incorporated and utilized in various devices such as drones and robots. This study focuses on object detection and recognition by employing sensor fusion. Object detection and recognition can be executed as a single function by incorporating sensors capable of recognition, such as image sensors, optical sensors, and propagation sensors. However, a single sensor has limitations with respect to object detection and recognition, and such limitations can be overcome by employing multiple sensors. In this paper, the performance of an eight-channel scanning LiDAR was evaluated and an object detection algorithm based on it was implemented. Furthermore, object detection characteristics during daytime and nighttime in a real road environment were verified. Obtained experimental results corroborate that an excellent detection performance of 92.87% can be achieved.