• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.504-509
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• 2008

Support Vector Machine (SVM) is one of powerful learning machine and has been applied to varying task with generally acceptable performance. The success of SVM for classification tasks in one domain is affected by features which represent the instance of specific class. Given the representative and discriminative features, SVM learning will give good generalization and consequently we can obtain good classifier. In this paper, we will assess the problem of feature choices for human detection tasks and measure the performance of each feature. Here we will consider HOG-family feature. As a natural extension of SVM, we combine SVM with Principal Component Analysis (PCA) to reduce dimension of features while retaining most of discriminative feature vectors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.639-641
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• 2012

People detection using HOG linear SVM classification has been successfully applied. Also, HOG combined with LBP, which reflects texture informations, shows improved performance. In this paper, we analyze various gamma correction methods. We also analyze results obtained using HOG+LBP methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.693-694
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• 2015

This paper present a decision method of middle ear disease which is developed in children and adults. In the proposed method, features are extracted from the middle ear disease images and normal images using HOG(histogram of oriented gradient) descriptor and the extracted features are learned by SVM(support vector machine) classifier. Input images are classified by SVM classifier based on the model of learning features. Experimental results show that the method yields accuracy of over 90% in decision.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.345-346
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• 2018

차종 별 교통량 자료는 도로의 유지관리나 분석 등의 행정 처리 업무에 필요한 기본 자료임과 동시에 각종 연구에 활용된다. 본 시스템은 그 일환으로서 화물차나 일반차량을 구분하여 특정 도로의 화물차 비율이나 교통량을 파악하는데 활용할 수 있다. 머신 러닝 알고리즘 중에서 높은 성능을 보이는 Support Vector Machine (SVM) 알고리즘을 이용하여 도로 위의 일반차량과 화물차를 구분하였다. 우선, 화물차와 일반차량의 차이를 구분하고자 각각의 영상에 대해 Histogram of Oriented Gradients (HOG) 기반 특징점을 추출하고 이에 따라 1 차원 벡터로 표현된 데이터를 SVM 으로 분류하여 구분한다.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.537-546
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• 2014

In this paper, we propose a method to detect interesting objects in inaccessible areas using high resolution satellite images. We define the interesting objects as a set of objects which have conceptually similar image patterns, not having exact sizes or shapes. In this paper, we developed a learning and classifier of Support Vector Machine (SVM) that extracts characteristic data for inputted images using Histogram of Gradients (HOG) feature and detects similar objects in other images using the characteristic data. As automatic search of interesting objects in our proposed method, we identify that our method provides reduced time and efforts for manual searching similar objects.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.205-208
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• 2012

지하철 실내의 표지판은 다양한 정보들을 포함하고 있다. 그 중에서 시설물의 형상을 단순화하여 나타낸 픽토그램 사인은 국제적으로 표준화 되어 있어 검출이 용이하다고 볼 수 있다. 일반적으로 객체를 인식하는 방법은 후보영역을 설정하는 검출단계와 후보영역을 인식하는 단계로 나뉘어 진다. 본 논문에서는 후보영역 검출단계에서 단일 값을 가지는 이진화로 픽토그램 영역이 분리가 잘 안되는 문제점을 해결하기 위하여 적응적 이진화를 사용하였고 인식을 위한 특징추출로 HOG서술자를 사용하고 학습과 인식에는 SVM을 사용하였다. 실험 결과를 통하여 HOG서술자로 픽토그램 사인을 인식하는 것이 적합한 것인지 확인한다.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.62-69
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• 2017

Accurate detection of traffic lights is very important for the advanced driver assistance system (ADAS). There have been many research developments in this area. However, conventional of image processing methods are usually sensitive to varying illumination conditions. This paper proposes a traffic light detection algorithm to overcome this situation. The proposed algorithm first detects the candidates of traffic light using the proposed color map and hue-saturation-value (HSV) Traffic lights are then detected using the conventional histogram of oriented gradients (HOG) descriptor and support vector machine (SVM). Finally, the proposed Multilayer HOG descriptor is used to determine the direction information indicated by traffic lights. The proposed algorithm shows a high detection rate in real-time.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1289-1301
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• 2015

Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.178-181
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• 2012

본 논문에서는 퍼지 콘트라스트와 HOG 기법을 이용한 감시 시스템을 제안한다. 제안된 감시 시스템은 감시 영상에서 명암 대비를 강조하기 위해 퍼지 콘트라스트 기법을 적용한 후, 감시 전/후 영상에 Substraction기법을 적용한다. Substraction 기법이 적용된 영상에서 히스토그램의 변화가 클 경우에는 침입자의 침입으로 간주한다. 침입으로 간주된 영상에서 감시 대상의 물체를 감시할 영상과 침입자를 실시간으로 추적하기 위한 영상으로 구분한다. 감시 대상의 물체를 감시할 영상에서는 퍼지 이진화를 적용한다. 퍼지 이진화를 적용한 영상에서 Blob 기법을 적용하여 객체화한 후, 침입된 침입자의 영상을 저장한다. 침입자를 실시간으로 추적할 영상에서는 HOG 기법을 적용한 후, SVM을 이용하여 움직이는 사람의 객체를 추적한다. 제안된 방법을 실시간 영상에 적용한 결과, 제안된 감시 시스템이 효율적으로 침입자를 감시하는 것을 확인할 수 있었다.

• Journal of Sensor Science and Technology
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• v.25 no.4
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• pp.285-290
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• 2016

Human sensing is an important but challenging technology. Unlike other methods for sensing humans, a vision sensor has many advantages, and there has been active research in automatic human detection in camera images. The combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is currently one of the most successful methods in vision-based human detection. However, extracting HOG features from an image is computer intensive, and it is thus hard to employ the HOG method in real-time processing applications. This paper describes an efficient solution to this speed problem of the HOG method. Our method obtains edge information of an image and finds candidate regions where humans very likely exist based on the distribution pattern of the detected edge points. The HOG features are then extracted only from the candidate image regions. Since complex HOG processing is adaptively done by the guidance of the simpler edge detection step, human detection can be performed quickly. Experimental results show that the proposed method is effective in various images.