• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.377-385
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• 2015

This paper proposes a novel forward vehicle tracking algorithm based on the WMIL(Weighted Multiple Instance Learning) equipped with a particle filter. In the proposed algorithm Haar-like features are used to train a vehicle object detector to be tracked and the location of the object are obtained from the recognition result. In order to combine both the WMIL to construct the vehicle detector and the particle filter, the proposed algorithm updates the object location by executing the propagation, observation, estimation, and selection processes involved in particle filter instead of finding the credence map in the search area for every frame. The proposed algorithm inevitably increases the computation time because of the particle filter, but the tracking accuracy was highly improved compared to Ababoost, MIL(Multiple Instance Learning) and MIL-based ones so that the position error was 4.5 pixels in average for the videos of national high-way, express high-way, tunnel and urban paved road scene.

• Smart Media Journal
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• v.5 no.1
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• pp.15-23
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• 2016

This paper proposes an effective algorithm of Korean license plate recognition. By applying Haar-like feature and Canny edge detection on a captured vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are classified using neural networks trained by backpropagation algorithm to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.700-703
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• 2016

OpenCV(Open Computer Vision)에서 제공하는 얼굴 검출 알고리즘은 Haar-like feature와 Cascade 방식을 이용하여 얼굴의 패턴을 찾아내 얼굴을 검출한다. 그러나 우연히 얼굴이 아닌 곳이 얼굴과 유사한 패턴일 경우, 얼굴로 인식하는 오류를 범하게 된다. 따라서 본 논문은 MCT(Modified Census Transform)와 신경망을 이용하여 잘못된 얼굴 검출 영역을 감소시키는 알고리즘을 제안한다. MCT는 다양한 조명 조건에서도 강인한 얼굴 영상의 지역적 구조 특징을 추출하기 위하여 사용되고, 신경망 알고리즘은 Haar-Cascade 알고리즘의 얼굴 검출 방법으로 검출된 영역이 실제로 얼굴인지 아닌지를 판단하기 위하여 사용된다. 실험에서 사용된 6개의 데이터들은 인터넷에서 수집한 것으로서, Haar-Cascade 알고리즘의 얼굴 검출 방법으로 얼굴을 검출하였을 때 오검출된 영역이 1개 이상 존재한다. 본 논문에서 제안한 알고리즘으로 실험한 결과, Haar-Cascade 알고리즘의 얼굴 검출 방법에 비하여 오검출된 영역이 감소된 것을 확인할 수 있었다.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.117-124
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• 2013

In this paper, we present a new method which efficiently estimates a face direction from a sequences of input video images in real time fashion. For this work, the proposed method performs detecting the facial region and major facial features such as both eyes, nose and mouth by using the Haar-like feature, which is relatively not sensitive against light variation, from the detected facial area. Then, it becomes able to track the feature points from every frame using optical flow in real time fashion, and determine the direction of the face based on the feature points tracked. Further, in order to prevent the erroneously recognizing the false positions of the facial features when if the coordinates of the features are lost during the tracking by using optical flow, the proposed method determines the validity of locations of the facial features using the template matching of detected facial features in real time. Depending on the correlation rate of re-considering the detection of the features by the template matching, the face direction estimation process is divided into detecting the facial features again or tracking features while determining the direction of the face. The template matching initially saves the location information of 4 facial features such as the left and right eye, the end of nose and mouse in facial feature detection phase and reevaluated these information when the similarity measure between the stored information and the traced facial information by optical flow is exceed a certain level of threshold by detecting the new facial features from the input image. The proposed approach automatically combines the phase of detecting facial features and the phase of tracking features reciprocally and enables to estimate face pose stably in a real-time fashion. From the experiment, we can prove that the proposed method efficiently estimates face direction.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.57-63
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• 2015

Compared to other emotion recognition technology, facial expression recognition technology has the merit of non-contact, non-enforceable and convenience. In order to apply to a psychological robot, vision technology must be able to quickly and accurately extract the face region in the previous step of facial expression recognition. In this paper, we remove the background from any image using the YCbCr skin color technology, and use Haar-like Feature technology for robust face detection. We got the result of improved processing speed and robust face detection by removing the background from the input image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.198-201
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• 2009

In this paper, an eye tracking method is presented by using on iterated spatial moment adapting weighted gray level that can accurately detect and track user's eyes under the complicated background. The region of face is detected by using Haar-like feature before extracting region of eyes to minimize an region of interest from the input picture of CCD camera. And the region of eyes is detected by using eigeneye based on the eigenface of Principal component analysis. And then feature points of eyes are detected from darkest part in the region of eyes. The tracking of eyes is achieved correctly by using iterated spatial moment adapting weighted gray level.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.3
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• pp.1-7
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• 2012

As personal information is utilized as an important user authentication means, a trustable certification means is being required. Recently, a research on the biometrics system using a part of the human body like a password is being attempted a lot. The face recognition technology using characteristics of the personal face among several biometrics technologies is easy in extracting features. In this paper, we implement a face recognition system for unattended reception interface. Our method is performed by two steps. Firstly the face is extracted using Haar-like feature method. Secondly the method combining PCA and LDA for face recognition was used. To assess the effectiveness of the proposed system, it was tested and experimental results show that the proposed method is applicable for unattended reception interface.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.403-409
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• 2016

Recently, harmful content (such as images and photos of nudes) has been widely distributed. Therefore, there have been various studies to detect and filter out such harmful image content. In this paper, we propose a new method using Haar-like features and an AdaBoost algorithm for robustly extracting navel areas in a color image. The suggested algorithm first detects the human nipples through color information, and obtains candidate navel areas with positional information from the extracted nipple areas. The method then selects real navel regions based on filtering using Haar-like features and an AdaBoost algorithm. Experimental results show that the suggested algorithm detects navel areas in color images 1.6 percent more robustly than an existing method. We expect that the suggested navel detection algorithm will be usefully utilized in many application areas related to 2D or 3D harmful content detection and filtering.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.85-88
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• 2017

보행자 검출을 위한 기술이 많이 개발되고 있으며 HOG(Histograms of oriented)와 haar-like feature를 이용한 특징값 검출을 통해 보행자를 검출하는 방법들이 대표적이라 할 수 있다. 하지만 이 방법들은 보행자가 사물에 가려졌을 때 보행자를 검출하지 못한다는 단점이 있다. 이에 본 논문에서는 haar-like feature와 adaboost 학습알고리듬을 이용하여 보행자를 검출하고 kalman filter를 이용하여 보행자가 특정 사물에 가려지는 것 과 같은 occlusion 문제를 해결하여 보행자 검출 성능을 높이고자 하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.477-480
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• 2018

Viola and Jones object detection algorithm is a representative face detection algorithm. The algorithm uses Haar-like features for face expression and uses a cascade-Adaboost algorithm consisting of strong classifiers, a linear combination of weak classifiers for classification. This algorithm requires several parameter settings for its implementation and the set values affect its performance. This paper analyzes face detection performance according to the parameters set in the algorithm.