• Journal of Multimedia Information System
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• 제3권4호
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• pp.119-128
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• 2016

This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• 대한의용생체공학회:의공학회지
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• 제32권4호
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• pp.336-344
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• 2011

This article describes a new recognition algorithm using correlation coefficient for intelligent wheelchair to avoid collision for elderly or disabled people. The correlation coefficient can be used to represent the relationship of two different areas. The algorithm has three steps: Firstly, we extract an edge vector using the Histogram of Oriented Gradients(HOG) which includes gradient information and unique magnitude for each cell. From this result, the correlation coefficients are calculated between one cell and others. Secondly, correlation coefficients are used as the weighting factors for normalizing the HOG cell. And finally, these features are used to classify or detect variable and complicated shapes of two wheelers using Adaboost algorithm. In this paper, we propose a new feature vectors which is calculated by weighted cell unit to classify with multiple view-based shapes: frontal, rear and side views($60^{\circ}$, $90^{\circ}$ and mixed angle). Our experimental results show that two wheeler detection system based on a proposed approach leads to a higher detection accuracy than the method using traditional features in a similar detection time.

• Journal of Multimedia Information System
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• 제2권4호
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• pp.303-310
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• 2015

Vulnerable road users such as bike, motorcycle, small automobiles, and etc. are easily attacked or threatened with bigger vehicles than them. So this paper suggests a new approach two-wheelers detection system riding on people based on modified histogram of oriented gradients (HOGs) which is weighted by curvature and local correlation coefficient. This correlation coefficient between two variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using the curvature of Gaussian and Histogram of Oriented Gradients (HOG) which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the correlation coefficient between the area of each cell and one of bike, can be used as the weighting factor in process for normalizing the HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. The experimental results validate the effectiveness of our proposed algorithm show higher than that of the traditional method and under challenging, such as various two-wheeler postures, complex background, and even conclusion.

• 방송공학회논문지
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• 제22권1호
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• pp.62-69
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• 2017

신호등 검출은 첨단운전자보조시스템에서 매우 중요하며 최근 신호등 검출 알고리즘의 연구가 활발히 진행 중이다. 그러나 기존의 영상처리 기반의 신호등검출 알고리즘은 조명의 변화에 민감하다는 문제점이 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 다음과 같은 신호등 검출 알고리즘을 제안한다. 먼저 제안하는 컬러맵과 HSV(hue-saturation-value)를 이용하여 신호등의 후보영역을 검출한다. 이후 검출된 신호등 후보영역으로부터 HOG(histogram of oriented gradient) 서술자와 SVM(support vector machine)을 이용하여 신호등을 검출한다. 검출된 신호등 영상을 이용하여 제안하는 Multilayer HOG 서술자를 이용하여 신호등의 방향 정보를 결정한다. 실험결과에서 확인할 수 있듯이 제안하는 알고리즘은 높은 검출성능과 실시간 처리가 가능하다.

• 전기학회논문지
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• 제64권7호
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• pp.1064-1073
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• 2015

In this study, we introduce the pedestrian detection system by using the feature of HOG-PCA and RBFNNs pattern classifier. HOG(Histogram of Oriented Gradient) feature is extracted from input image to identify and recognize a object. And a dimension is reduced for improving performance as well as processing speed by using PCA which is a typical dimensional reduction algorithm. So, the feature of HOG-PCA through the dimensional reduction by using PCA leads to the improvement of the detection rate. FCM clustering algorithm is used instead of gaussian function to apply the characteristic of input data as well and connection weight is used by polynomial expression such as constant, linear, quadratic and modified quadratic. Finally, INRIA person database known as one of the benchmark dataset used for pedestrian detection is applied for the performance evaluation of the proposed classifier. The experimental result of the proposed classifier are compared with those studied by Dalal.

• 스마트미디어저널
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• 제4권2호
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• pp.26-33
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• 2015

본 논문은 영상에서 효과적으로 보행자를 삭제하는 자동 삭제 시스템을 제안한다. 첫 번째로 Histogram of Oriented Gradient(HOG) / Linear-Support Vector Machine(L-SVM)분류기를 이용하여 보행자를 찾고, 참조영상으로부터 적절한 배경을 습득하여 삭제될 보행자를 대체한다. 배경은 참조영상 내에서 검색하며 변경된 feather blender 연산은 대체 영역의 경계를 자연스럽게 만든다. 기존에 존재하던 대부분의 시스템이 수동인 것에 반해 제안된 시스템은 자동으로 객체를 검출하고 자연스러운 배경을 생성한다. 실험결과 대체된 영역의 PSNR 평균은 19.246으로 측정되었다.

• 반도체디스플레이기술학회지
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• 제14권2호
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• pp.29-34
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• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제16권6호
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• pp.654-662
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• 2010

본 논문은 2단계 연속(cascade) 방법을 이용한 향상된 보행자/비보행자 인식 알고리즘을 제안한다. 인식을 위한 분류기로는 약한 분류기를 강한 분류기로 만드는 아다부스트 알고리즘을 적용하였다. 먼저 두 가지 특징벡터를 추출 한다: (i) 기존의 기울기 히스토그램(HOG) 특성과 (ii) 한 점이 가지는 곡률특성 네 가지를 이용한 곡률-HOG를 제안하고 이용하였다. 그 다음 훈련 영상을 통하여 두 가지의 특징 벡터에 대해 약한 분류기로부터 강한 분류기를 얻었으며, 인식은 입력 영상으로부터 하나의 특징을 선택하여 이미 만들어진 강한 분류기를 통하여 1차적인 인식과 오인식을 실시하며, 오인식된 영상에 대해 2차적인 특징을 투입하여 이에 해당하는 강한 분류기를 통하여 2단계 아다부스트 알고리즘을 적용하여 최종적인 인식결과를 얻는다. 두 가지의 서로 다른 특성 벡터를 이용하여 연속 방법에 의한 2단계 아다부스트 알고리즘을 적용한 결과 기존의 실험 방법보다 더 정확한 인식 결과를 얻을 수 있었다.

• Journal of Computing Science and Engineering
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• 제9권1호
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• pp.29-38
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• 2015

Human action recognition has become an important research topic in computer vision area recently due to many applications in the real world, such as video surveillance, video retrieval, video analysis, and human-computer interaction. The goal of this paper is to evaluate descriptors which have recently been used in action recognition, namely Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF). This paper also proposes new descriptors to represent the change of points within each part of a human body, caused by actions named as Histogram of Changing Points (HCP) and so-called Average Speed (AS) which measures the average speed of actions. The descriptors are combined to build a strong descriptor to represent human actions by modeling the information about appearance, local motion, and changes on each part of the body, as well as motion speed. The effectiveness of these new descriptors is evaluated in the experiments on KTH and Hollywood datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권12호
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• pp.6000-6017
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• 2018

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment.