• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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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 the Korea Convergence Society
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• v.10 no.6
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• pp.7-14
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• 2019

In this paper, we propose a Cascade Classifier face detection method using the Haar-like feature, which is complemented by the Flood Fill algorithm for lossy areas due to illumination and shadow in YCbCr color space extraction. The Cascade Classifier using Haar-like features can generate noise and loss regions due to lighting, shadow, etc. because skin color extraction using existing YCbCr color space in image only uses threshold value. In order to solve this problem, noise is removed by erosion and expansion calculation, and the loss region is estimated by using the Flood Fill algorithm to estimate the loss region. A threshold value of the YCbCr color space was further allowed for the estimated area. For the remaining loss area, the color was filled in as the average value of the additional allowed areas among the areas estimated above. We extracted faces using Haar-like Cascade Classifier. The accuracy of the proposed method is improved by about 4% and the detection rate of the proposed method is improved by about 2% than that of the Haar-like Cascade Classifier by using only the YCbCr color space.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.193-198
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• 2022

Recently, place that you need to check wearing mask and body temperature to prevent the proliferation of COVID-19 increased. But these things often measured by man manually or by machine one by one, result may be different by measuring ways, so it wastes workforce. Also, the machine generally just measures the highest temperature of the face, criteria for fever can't be trusted too. A bottleneck may occur due to crowding of people at the entrance, and because most of the measurement sites are at one entrance, it is inconvenient to track the movement of COVID-19 Confirmed cases. Thus, in this study, we intend to propose a method for suppressing the spread of infection by automatically classifying and displaying in real time using camera, thermal camera, Haar Cascade, and result selection algorithm.

• Annual Conference of KIPS
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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.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.51-57
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• 2017

Digital signage is becoming part of daily life across a wide range of visual advertisements segments market used in stations, hotels, retail stores, hotels, etc. The current digital signage system used in market is generally works on limited user interactivity with static contents. In this paper, a new approach is proposed using computer vision based dynamic audience adaptive cost-effective digital signage system. The proposed design uses the Camera attached Raspberry Pi Open source platform to employ the real-time audience interaction using computer vision algorithms to extract facial features of the audience. The real-time facial features are extracted using Haar Cascade algorithm which are used for audience gender specific rendering of dynamic digital signage content. The audience facial characterization using Haar Cascade is evaluated on the FERET database with 95% accuracy for gender classification. The proposed system, developed and evaluated with male and female audiences in real-life environments camera embedded raspberry pi with good level of accuracy.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.33-36
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• 2021

본 논문에서는 인간의 표정과 목소리를 기반으로 한 감정 분석기를 제안한다. 제안하는 분석기들은 수많은 인간의 표정 중 뚜렷한 특징을 가진 표정 7가지를 별도의 클래스로 구성하며, DNN 모델을 수정하여 사용하였다. 또한, 음성 데이터는 학습 데이터 증식을 위한 Data Augmentation을 하였으며, 학습 도중 과적합을 방지하기 위해 콜백 함수를 사용하여 가장 최적의 성능에 도달했을 때, Early-stop 되도록 설정했다. 제안하는 표정 감정 분석 모델의 학습 결과는 val loss값이 0.94, val accuracy 값은 0.66이고, 음성 감정 분석 모델의 학습 결과는 val loss 결과값이 0.89, val accuracy 값은 0.65로, OpenCV 라이브러리를 사용한 모델 테스트는 안정적인 결과를 도출하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.17-19
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• 2021

본 논문에서는 코로나19 사태에 대비하여 실시간으로 마스크를 제대로 쓴 사람과 제대로 쓰지 않은 사람을 구분하는 시스템을 제안한다. 이 시스템을 사용하기 위하여 모델 학습 시에 합성곱 신경망(CNN : Convolutional Neural Networks)를 사용한다. 학습된 모델을 토대로 영상에 적용 시 하르 특징 분류기(Haar Cascade Classifier)로 얼굴을 탐지하여 마스크 여부를 판단한다.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.588-594
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• 2021

Due to the recent proliferation of video contents, previous contents expressed as the character or the picture are being replaced to video and growth of video contents is being boosted because of emerging new platforms. As this accelerated growth has a great impact on the process of universalization of technology for ordinary people, video production and editing technologies that were classified as expert's areas can be easily accessed and used from ordinary people. Due to the development of these technologies, tasks like that recording and adjusting that depends on human's manual involvement could be automated through object tracking technology. Also, the process for situating the object in the center of the screen after finding the object to record could have been automated. Because the task of setting the object to be tracked is still remaining as human's responsibility, the delay or mistake can be made in the process of setting the object which has to be tracked through a human. Therefore, we propose a novel object tracking technique of CMT combining the face detection technique utilizing Haar cascade classifier. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the smartphone in real time.

• Convergence Security Journal
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• v.22 no.1
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• pp.29-38
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• 2022

The importance of prevention of epidemics is increasing due to the serious spread of infectious diseases. For prevention of epidemics, we need to focus on the non-contact industry. Therefore, in this paper, a face recognition door lock that controls access through non-contact is designed and developed. First very simple features are combined to find objects and face recognition is performed using Haar-based cascade algorithm. Then the texture of the image is binarized to find features using LBPH. An non-contact door lock system which composed of Raspberry PI 3B+ board, an ultrasonic sensor, a camera module, a motor, etc. are suggested. To verify actual performance and ascertain the impact of light sources, various experiment were conducted. As experimental results, the maximum value of the recognition rate was about 85.7%.

• 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.