• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.203-206
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• 2004

본 논문에서는 다양하고 실재적인 조명과 얼굴방향에 관계없이 원활하게 실시간으로 눈을 추적하는 방법을 제안하고자 한다. 기존의 능동적 적외선 조명을 이용한 대다수의 눈 추적장치들은 밝은 동공효과를 이용하고 있다. 그러나, 눈 깜박임, 외부 조명 간섭과 같은 여러 가지 요소로 인하여 동공들이 충분하게 밝게 나타나지 않는 경우가 많이 있다. 그러므로, 본 논문에서는 능동적 적외선 조명을 기반으로 한 칼만 필터링을 이용한 객체 추적 방법과 전형적인 외관을 기반으로 객체 인식 방법을 결합함으로써, 외부 조명의 간섭으로 밝은 동공 효과가 나타나지 않는 경우에도 견실하게 눈을 검출하고 추적 할 수 있는 방법을 제안한다. 눈 검출과 추적을 위해 SVM과 평균 이동 추적 방법을 사용하였고, 적외선 조명과 카메라를 포함한 영상 획득 장치를 구성하여 기존의 방법과 비교 실험한 결과 제안된 방법은 일부 피검자의 경우 100% 완벽하게 눈 추적을 할 수 있음을 보여 주었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.9-12
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• 2019

지난 수 년 동안 계속해서 일반 실상 카메라를 이용한 영상분석기술에 대한 연구가 활발히 진행되고 있다. 최근에는 딥러닝 기술을 적용한 지능형 영상분석기술로 발전해 왔으며 국방기지방호, CCTV, 사용자 얼굴인식, 머신비전, 자동차, 드론 산업이 활성화되면서 많은 시너지를 효과를 일으키고 있다. 그러나 어두운 밤과 안개, 날씨, 연기 등 다양한 여건에서 따라서 카메라의 영상분석 정확성 감소와 오류가 수반될 수 있으며 일반적으로 딥러닝 기술을 활용하기 위해서는 고사양의 GPU를 필요로 하기 때문에 다른 추가적인 시스템이 요구된다. 이에 본 연구에서는 열적외선 영상의 객체 검출에 적용하기 위해 SSD(Single Shot MultiBox Detector) 기반의 경량적인 MobilNet 네트워크로 재구성하여, 모바일 기기 등 낮은 사양의 낮은 임베디드 시스템에서도 활용 할 수 있는 방법을 제안한다. 모의 실험결과 제안된 방식의 모델은 적외선 열화상 카메라에서 객체검출과 학습시간이 줄어든 것을 확인 할 수 있었다.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.155-160
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• 2014

In this paper, an algorithm using RGB-depth camera is proposed to detect smoke in interrior. RGB-depth camera, the Kinect provides RGB color image and depth information. The Kinect sensor consists of an infra-red laser emitter, infra-red camera and an RGB camera. A specific pattern of speckles radiated from the laser source is projected onto the scene. This pattern is captured by the infra-red camera and is analyzed to get depth information. The distance of each speckle of the specific pattern is measured and the depth of object is estimated. As the depth of object is highly changed, the depth of object plain can not be determined by the Kinect. The depth of smoke can not be determined too because the density of smoke is changed with constant frequency and intensity of infra-red image is varied between each pixels. In this paper, a smoke detection algorithm using characteristics of the Kinect is proposed. The region that the depth information is not determined sets the candidate region of smoke. If the intensity of the candidate region of color image is larger than a threshold, the region is confirmed as smoke region. As results of simulations, it is shown that the proposed method is effective to detect smoke in interior.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.1-13
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• 2017

In This Paper, we find the Target Candidate Region and the Location of the Candidate Region by Performing the Morphological Difference Calculation and Pixel Labeling for Robust Small Target Detection in Infrared Image with low SNR. Conventional Target Detection Methods based on Morphology Algorithms are low in Detection Accuracy due to their Vulnerability to Clutter in Infrared Images. To Address the Problem, Target Signal Enhancement and Background Clutter Suppression are Achieved Simultaneously by Combining Moravec Algorithm and LCM (Local Contrast Measure) Algorithm to Classify the Target and Noise in the Candidate Region. In Addition, the Proposed Algorithm can Efficiently Detect Multiple Targets by Solving the Problem of Limited Detection of a Single Target in the Target Detection method using the Morphology Operation and the Gaussian Distance Function Which were Developed for Real time Target Detection.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.183-188
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• 2015

In this paper, it is proposed to detect and track pedestrian and analyse tracking performance with nighttime CCTV video. The detection is performed by a cascade classifier with Haar-like feature trained with Adaboost algorithm. Tracking pedestrian is performed by a particle filter. As results of experiments, it is introduced that efficient number of particles and the distributions are applied to track pedestrian at the night-time. Performance of detection and tracking is verified with nighttime CCTV video that is obtained at alleys etc.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1213-1218
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• 2018

Recently introduced Mask R - CNN presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation mask of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask R - CNN is an algorithm that extends Faster R - CNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. The mask R - CNN is added to the high - speed R - CNN which training is easy and fast to execute. Also, it is easy to generalize the mask R - CNN to other tasks. In this research, we propose an infrared image detection algorithm based on R - CNN and detect heating elements which can not be distinguished by RGB images. As a result of the experiment, a heat-generating object which can not be discriminated from Mask R-CNN was detected normally.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.41-44
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• 2022

Mean-Shift 영상 분할은 객체 검출을 위한 영상 전처리 방법으로써, 영상 처리 및 패턴 인식 분야에서 널리 사용되는 방법이다. 영상 분할은 영역 기반과 에지 기반 방식으로 나누어지며 대표적으로 FCM, Quickshift, Felzenszwalb, SLIC 알고리즘 등 이 있다. 언급한 영상 분할 방법들은 Mean-Shift 영상 분할에 비해서 빠른 속도로 실행시킬 수 있지만, 형태적 특징이 훼손되고 하나의 객체가 여러 세그멘테이션으로 분할된다는 단점을 가지고 있다. 본 논문에서는 소형 객체를 탐지하기 위한 고속화된 Mean-Shift 영상 분할과 객체의 형태적 특징을 이용하여 객체를 탐지하는 방법을 제안한다. 하드웨어 리소스가 제한된 신호처리기에 제안하는 알고리즘을 수행하기 위하여 Mean-Shift 영상 분할에서 필터링 과정을 고속화 하였고, 적외선 영상 내 영상 전처리 수행을 통해 잡음 제거 후 Mean-Shift 영상 분할 방법을 수행함으로써, 객체의 형태적 특징을 잘 살려서 영상 분할을 할 수 있도록 하였다. 또한 각 세그멘테이션의 크기, 너비, 높이, 밝기 정보와 형태적 특징점을 이용한 객체 탐지 방법을 제안한다.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.6
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• pp.591-601
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• 2021

Recently, infrared object detection(IOD) has been extensively studied due to the rapid growth of deep neural networks(DNN). Adversarial attacks using imperceptible perturbation can dramatically deteriorate the performance of DNN. However, most adversarial attack works are focused on visible image recognition(VIR), and there are few methods for IOD. We propose deep learning-based adversarial attacks for IOD by expanding several state-of-the-art adversarial attacks for VIR. We effectively validate our claim through comprehensive experiments on two challenging IOD datasets, including FLIR and MSOD.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.11-18
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• 2022

The use of multi-spectral cameras is essential for day and night pedestrian detection. In this paper, a color camera and a thermal imaging infrared camera were used to detect pedestrians near a crosswalk for 24 hours at an intersection with a high risk of traffic accidents. For pedestrian detection, the YOLOv5 object detector was used, and the detection performance was improved by using color images and thermal images at the same time. The proposed system showed a high performance of 0.940 mAP in the day/night multi-spectral (color and thermal image) pedestrian dataset obtained from the actual crosswalk site.