• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.293-297
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• 2007

This thesis suggests a new algorithm to detects multiple moving objects using a CMODE(Correct Multiple Object DEtection) method in the color images acquired in real-time and to track the interested pedestrian using motion and hue information. The multiple objects are detected, and then shaking trees or moving cars are removed using structural characteristics and shape information of the man , the interested pedestrian can be detected, The first similarity judgment for tracking an interested pedestrian is to use the distance between the previous interested pedestrian's centroid and the present pedestrian's centroid. For the area where the first similarity is detected, three feature points are calculated using k-mean algorithm, and the second similarity is judged and tracked using the average hue value for the $3{\times}3$ area of each feature point. The zooming of camera is adjusted to track an interested pedestrian at a long distance easily and the FOV(Field of View) of camera is adjusted in case the pedestrian is not situated in the fixed range of the screen. As a experiment results, comparing the suggested CMODE method with the labeling method, an average approach rate is one fourth of labeling method, and an average detecting time is faster three times than labeling method. Even in a complex background, such as the areas where trees are shaking or cars are moving, or the area of shadows, interested pedestrian detection is showed a high detection rate of average 96.5%. The tracking of an interested pedestrian is showed high tracking rate of average 95% using the information of situation and hue, and interested pedestrian can be tracked successively through a camera FOV and zooming adjustment.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.317-326
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• 2004

This paper presents a new method for the detection of multiple pedestrians and tracking of a specific pedestrian using color information from a moving camera. We first extract motion vector on the input image using BMA. Next, a difference image is calculated on the basis of the motion vector. The difference image is converted to a binary image. The binary image has an unnecessary noise. So, it is removed by means of the proposed noise deletion method. Then, we detect pedestrians through the projection algorithm. But, if pedestrians are very adjacent to each other, we separate them using RGB color information. And we track a specific pedestrian using RGB color information in center region of it. The experimental results on our test sequences demonstrated the high efficiency of our approach as it had shown detection success ratio of 97％ and detection failure ratio of 3％ and excellent tracking.

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.421-429
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• 2015

This paper is about an approach for pedestrian detection and tracking with infrared imagery. We used the CUDA(Computer Unified Device Architecture) that is a parallel processing language in order to improve the speed of video-based pedestrian detection and tracking. The detection phase is performed by Adaboost algorithm based on Haar-like features. Adaboost classifier is trained with datasets generated from infrared images. After detecting the pedestrian with the Adaboost classifier, we proposed a particle filter tracking strategies on HSV histogram feature that exploit adaptively at the same time. The proposed approach is implemented on an NVIDIA Jetson TK1 developer board that is full-featured device ideal for software development within the Linux environment. In this paper, we presented the results of parallel processing with the NVIDIA GPU on the CUDA development environment for detection and tracking of pedestrians. We compared the object detection and tracking processing time for night-time images on both GPU and CPU. The result showed that the detection and tracking speed of the pedestrian with GPU is approximately 6 times faster than that for CPU.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1351-1352
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• 2015

본 논문에서는 지능형 영상 감시 시스템에서 보행자를 검출하고 추적을 수행하기 위해 은닉층 활성함수에 가우시안 대신 FCM를 사용한 RBFNNs 패턴분류기와 객체 추적 알고리즘인 Mean Shift를 융합한 시뮬레이터를 개발한다. 시뮬레이터는 검출부과 추적부로 나누며, 검출부에서는 입력 영상으로부터 기울기의 방향성을 이용한 HOG(Histogram of Oriented Gradient) 특징을 구하고 빠른 처리속도를 위해 PCA 알고리즘을 통해 차원수를 축소하고 pRBFNNs 패턴분류기를 통해 보행자를 검출 한다. 다음 추적부에서 객체 추적 알고리즘인 Mean Shift를 이용하여 검출된 보행자 추적을 수행한다.

• Journal of IKEEE
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• v.17 no.4
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• pp.469-477
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• 2013

This paper enhances the speed of a pedestrian detection using an estimation of feature information based on integral image. Pedestrian model or input image should be resized to the size of various pedestrians. In case that the size of pedestrian model would be changed, pedestrian models with respect to the size of pedestrians should be required. Reducing the size of pedestrian model, however, deteriorates the quality of the model information. Since various features according to the size of pedestrian models should be extracted, repetitive feature extractions spend the most time in overall process of pedestrian detection. In order to enhance the processing time of feature extraction, this paper proposes the fast extraction of pedestrian features based on the estimate of integral image. The efficiency of the proposed method is evaluated by comparative experiments with the Channel Feature and Adaboost training using INRIA person dataset.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1302-1304
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• 2015

컴퓨터 비전에서 다수의 보행자 궤적을 생성하는 문제는 여전히 어려운 문제이다. 전경에서 추출된 보행자 윤곽은 음영과 밝기 등의 문제로 윤곽이 명확하지 않고, 보행자들이 서로 다른 방향으로 움직이며 상호작용을 한다. 이로 인해 보행자를 식별하고 궤적을 생성하기에는 다소 어려움이 있다. 우리는 의사결정 트리를 사용하여 보행자 영역의 병합과 분할 상황을 개별 분리된 보행자로 검출한다. 검출된 개별 보행자는 점 대응 알고리즘으로 각 보행자의 궤적을 생성한다. 우리는 수정된 $A^*$ 검색 알고리즘으로 새로운 휴리스틱 점 대응 알고리즘을 소개한다. 우리의 실험은 PETS2010 데이터 세트로 구현되고 실험했다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.176-190
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• 2019

The prevention of pedestrian accidents in crosswalks and intersections is very important. The C-ITS services provide a warning service for preventing accidents between cars and pedestrians. In the current pedestrian collision prevention warning service according to the C-ITS standard, however, it is difficult to provide real-time service because it detects pedestrians from a video-analysis server in the control center and sends service messages through the ITS system. This paper proposes a pedestrian collision-prevention warning system that detects pedestrians in the local field using an edge camera and sends a warning message directly to the driver through a roadside unit. An evaluation showed that the proposed system could deliver the pedestrian collision prevention-warning message to the driver satisfying the delay time within the 300 ms required by the C-ITS standard, even in the worst case.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.83-89
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• 2021

In this paper, we propose an algorithm that provide services to consumers who want to conduct business by scientifically and systematically analyzing the number of pedestrians in a specific area over a specific period of time. In this paper, we proposed the algorithm to analyze the commercial area using the pedestrian-detect algorithm in the particular region using YOLO, one of the deep learning techniques. And with one image per minute in the images, the number of pedestrians is identified and this information is used for the analysis of business district on interesting area and time, systematically and objectively.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.348-350
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• 2012

본 논문에서는 열 영상카메라를 통해 입력 받은 영상을 CS-LBP(Center-symmetric LBP)와 랜덤 포레스트(Random forest)를 이용하여 보행자 휴먼 객체를 검출하는 방법을 제안한다. 우선 불필요한 후보영역을 줄이기 위해 열 영상의 표준편차, 밝기 평균, 밝기 최대값을 이용하여 이진화하고, 신체부위 중 가장 발열이 강한 얼굴부위를 핫스팟 영역으로 설정한다. 그 후, 핫스팟 영역에서 CS-LBP특징을 추출하여 결정 트리의 앙상블인 랜덤 포레스트 분류기를 이용하여 최종적인 보행자 휴먼 객체를 검증한다. CS-LBP와 랜덤 포레스트 분류기를 통해 실시간 보행자 객체의 검출이 가능하고, 높은 검출 성능을 나타내었다.

• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.361-368
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• 2018

As autonomous driving research is rapidly developing, pedestrian detection study is also successfully investigated. However, most of the study utilizes color image datasets and those are relatively easy to detect the pedestrian. In case of color images, the scene should be exposed by enough light in order to capture the pedestrian and it is not easy for the conventional methods to detect the pedestrian if it is the other case. Therefore, in this paper, we propose deep neural network (DNN)-based multi-spectrum pedestrian detection method using color and thermal images. Based on single-shot multibox detector (SSD), we propose fusion network structures which simultaneously employ color and thermal images. In the experiment, we used KAIST dataset. We showed that proposed SSD-H (SSD-Halfway fusion) technique shows 18.18% lower miss rate compared to the KAIST pedestrian detection baseline. In addition, the proposed method shows at least 2.1% lower miss rate compared to the conventional halfway fusion method.