• 한국산학기술학회논문지
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• 제14권8호
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• pp.3971-3975
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• 2013

본 논문은 핸드 제스쳐에 의해 증강현실 내의 가상 객체 제어기술로, HOG기반의 핸드 제스쳐 인식을 제안하고 있다. 인식을 위한 특징점들은 HOG불럭들에 의하여 결정되며, 크기가 다른 여러 불럭들을 시험하여 가장 적절한 불럭구성을 결정하며, AdaBoostSVM기법을 사용하여 분류 목적에 가장 적절한 불럭들을 추출한다. 실험 결과 핸드 제스쳐 인식률은 94% 이었다.

• Journal of Multimedia Information System
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• 제4권1호
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• pp.33-38
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• 2017

This article suggests a new algorithm for detecting two-wheelers on the road that have various shapes according to viewpoints. Because of complicated shapes, it is more difficult than detecting a human. In general, the Histograms of Oriented Gradients(HOG) feature is well known as a useful method of detecting a standing human. We propose a method of detecting a human on a two-wheelers using the spatial relocation of HOG (Histogram of Oriented Gradients) features. And this paper adapted the contrast method which is generally using in the image process to improve the detection rate. Our experimental results show that a two-wheelers detection system based on proposed approach leads to higher detection accuracy, less computation, and similar detection time than traditional features.

• 제어로봇시스템학회논문지
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• 제18권4호
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• pp.353-358
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• 2012

Human detection is a challenging task in many fields because it is difficult to detect humans due to their variable appearance and posture. Furthermore, it is also hard to track the detected human because of their dynamic and unpredictable behavior. The evaluation speed of method is also important as well as its accuracy. In this paper, we propose unified detection and tracking method for humans using Gaussian-PSO (Gaussian Particle Swarm Optimization) with the HOG (Histograms of Oriented Gradients) features to achieve a fast and accurate performance. Keeping the robustness of HOG features on human detection, we raise the process speed in detection and tracking so that it can be used for real-time applications. These advantages are given by a simple process which needs just one linear-SVM classifier with HOG features and Gaussian-PSO procedure for the both of detection and tracking.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 춘계학술대회
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• pp.639-641
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• 2012

기울기 값과 방향성의 특징 값을 이용하는 HOG와 선형SVM을 분류기로 사용하는 사람검출 기법은 슬라이딩 윈도우 기반 사람검출에 성공적으로 적용되었다. 또한 텍스처 구별에 강인한 특징을 가지고 있는 LBP를 HOG와 함께 특징 서술자로 적용하는 방법은 서로의 단점을 상호 보안하여 향상된 성능을 보인다. 본 논문에서는 기존 HOG제곱근 Gamma 보정을 다양한 Gamma 보정 값으로 대체하고 성능을 분석한다.

• 센서학회지
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• 제29권3호
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• pp.194-200
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• 2020

Human detection is an important aspect in many video-based sensing and monitoring systems. Studies have been actively conducted for the automatic detection of humans in camera images, and various methods have been proposed. However, there are still problems in terms of performance and computational cost. In this paper, we describe a method for efficient human detection in the field of view of a camera, which may be static or moving, through multiple processing steps. A detection line is designated at the position where a human appears first in a sensing area, and only the one-dimensional gray pixel values of the line are monitored. If any noticeable change occurs in the detection line, corner detection and optical flow computation are performed in the vicinity of the detection line to confirm the change. When significant changes are observed in the corner numbers and optical flow vectors, the final determination of human presence in the monitoring area is performed using the Histograms of Oriented Gradients method and a Support Vector Machine. The proposed method requires processing only specific small areas of two consecutive gray images. Furthermore, this method enables operation not only in a static condition with a fixed camera, but also in a dynamic condition such as an operation using a camera attached to a moving vehicle.

• 전기전자학회논문지
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• 제18권1호
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• pp.106-113
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• 2014

보행자 검출기는 보안이 필요한 곳에서 모니터링을 하거나 특정 장소를 드나드는 사람의 수를 셀 때, 운전 중 차도에 뛰어드는 사람을 감지할 때 등 상황에 따라 여러 목적으로 응용될 수 있다. 이와 관련한 연구는 많이 진행되어 왔지만, 임베디드 시스템에서는 제한된 컴퓨팅 능력으로 인해 검출 속도가 느리다는 문제가 있다. 본 논문에서는 입력 영상에서 배경 부분을 빠르게 제거하여 검출 속도를 향상하는 방법과 ARM SIMD 아키텍처에서 NEON 병렬화 기법을 이용하여 검출 속도를 향상하는 방법을 제시한다. 제시한 방법으로 구현한 검출기는 INRIA Person Dataset을 이용하여 테스트한 결과 기존에 비해 3.01배의 향상된 속도를 나타냈다.

• 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.

• ETRI Journal
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• 제35권6호
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• 전자공학회논문지
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• 제51권10호
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• pp.190-196
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• 2014

본 논문에서는 상용 스마트폰에서의 첨단운전자보조시스템(ADAS)을 위해 모바일 플랫폼에 최적화된 cascade 방식의 HOG 특징을 이용한 보행자 검출 방법을 제안한다. 제한된 모바일 플랫폼 자원을 효율적으로 사용하기 위해 OpenCL 병렬처리 라이브러리를 이용하였고 크게 두 가지 방법으로 수행속도를 향상시켰다. 첫째, 호스트 코드에서 OpenCL 프로그램 빌드 옵션을 특정하고 작업 그룹 크기를 조절하였다. 둘째, 커널 코드에서 지역 메모리와 LUT 등을 사용하여 가속화하였다. 성능 평가를 위하여 널리 알려진 영상처리 라이브러리인 OpenCV for Android 함수의 모바일 CPU 수행 결과와 비교하였으며 실험 결과, OpenCV의 hogcascade 함수보다 25% 향상된 처리속도를 보였다.

• ETRI Journal
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• 제35권6호
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• pp.1038-1047
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• 2013

Augmented reality (AR) is currently being applied actively to commercial products, and various types of intelligent AR systems combining both the Global Positioning System and computer-vision technologies are being developed and commercialized. This paper suggests an in-vehicle head-up display (HUD) system that is combined with AR technology. The proposed system recognizes driving-safety information and offers it to the driver. Unlike existing HUD systems, the system displays information registered to the driver's view and is developed for the robust recognition of obstacles under bad weather conditions. The system is composed of four modules: a ground obstacle detection module, an object decision module, an object recognition module, and a display module. The recognition ratio of the driving-safety information obtained by the proposed AR-HUD system is about 73%, and the system has a recognition speed of about 15 fps for both vehicles and pedestrians.