• 로봇학회논문지
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• 제7권2호
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• pp.113-119
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• 2012

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

• 전기학회논문지
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• 제64권7호
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• 대한임베디드공학회논문지
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• 제18권3호
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• pp.125-132
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• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• 반도체디스플레이기술학회지
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• 제16권3호
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• pp.78-81
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• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• 한국컴퓨터정보학회논문지
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• 제27권1호
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• pp.1-8
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• 2022

본 논문에서는 실시간 객체 탐지(Real-time Object Detection)가 가능한 YOLOv4 모델과 DeepSORT 알고리즘을 활용한 객체 추적(Object Tracking) 기술을 활용하여 CCTV 영상 이미지 기반의 화재 탐지 시스템을 제안한다. 화재 탐지 모델은 10800장의 학습용 데이터로부터 학습되었으며 1000장의 별도 테스트 셋을 통해 검증되었다. 이후 DeepSORT 알고리즘을 통해 탐지된 화재 영역을 추적하여 단일 이미지 내의 화재 탐지율과 영상 내에서의 화재 탐지 유지성능을 증가시켰다. 영상 내의 한 프레임 혹은 단일 이미지에 대한 화재 탐지 속도는 장당 0.1초 이내로 실시간 탐지가 가능함을 확인하였으며 본 논문의 AI 화재 탐지 시스템은 기존의 화재 사고 탐지 시스템 보다 안정적이고 빠른 성능을 지니고 있어 화재현장에 적용 시 화재를 조기 발견하여 빠른 대처 및 발화단계에서의 진화가 가능할 것으로 예상된다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권1호
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• pp.9-14
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• 2017

최근 컴퓨터 기술의 발전과 함께 임베디드 기기 또한 다양한 기능을 갖추기 시작했다. 본 연구에서는 최근 활발하게 진행되고 있는 영상센서를 사용한 임베디드 기기 등 자원이 적은 기기에서 효율적인 얼굴 추적 방식을 제안한다. 정확한 얼굴을 얻기 위하여 MB-LBP 특징을 사용한 얼굴 검출 방식을 사용했으며, 다음 영상에서 얼굴 객체 추적을 위하여 얼굴 검출시 얼굴 주변 영역(Region of Interest)을 지정하였다. 그리고 얼굴을 검출을 못하는 영상에서는 기존의 객체 추적 방식인 CAM-Shift를 사용해 객체를 추적해 객체 정보의 손실 없이 정보를 유지할 수 있도록 하였다. 본 연구는 기존 연구와의 비교를 통하여 객체 추적 시스템의 정확성과 빠른 성능을 확인하였다.

• 디지털융복합연구
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• 제12권8호
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• pp.337-343
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• 2014

본 논문은 감지와 통신 데이터 전송량의 관점에서 서베일런스 네트워크에서 움직이는 객체를 추적하기 위하여 전송 데이터를 감소시키는 최소 윤곽선 추적 알고리즘을 제안한다. 이 알고리즘은 객체 추적에 대한 감지를 수행하고 서버와의 영상 데이터 전송 시 영상 데이터 전송량을 줄임으로써 서버와의 통신 부하를 최소화한다. 이 알고리즘은 객체의 운동학을 기초로 최소 추적 영역을 사용한다. 객체의 운동학의 모델링은 예정된 시간 안에서 이동할 수 있는 객체에 의해 운동 역학적으로 방문될 수 없는 추적 영역의 부분을 제거하는 것으로써 시작한다. 실시간으로 객체를 검출하는 응용 분야에서 대량의 영상 데이터를 전송시에 전송 부하를 줄일 수 있는 효과가 있다.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.1017-1028
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• 2019

Pedestrian tracking is a particular object tracking problem and an important component in various vision-based applications, such as autonomous cars and surveillance systems. Following several years of development, pedestrian tracking in videos remains challenging, owing to the diversity of object appearances and surrounding environments. In this research, we proposed a tracking-by-detection system for pedestrian tracking, which incorporates a convolutional neural network (CNN) and color information. Pedestrians in video frames are localized using a CNN-based algorithm, and then detected pedestrians are assigned to their corresponding tracklets based on similarities between color distributions. The experimental results show that our system is able to overcome various difficulties to produce highly accurate tracking results.

• 스마트미디어저널
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• 제5권1호
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• pp.78-87
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• 2016

The major problems of recent object tracking methods are related to the inefficient detection of moving objects due to occlusions, noisy background and inconsistent body motion. This paper presents a robust method for the detection and tracking of a moving in infrared animal videos. The tracking system is based on adaptive optical flow generation, Gaussian mixture and Kalman filtering. The adaptive Gaussian model of optical flow (GMOF) is used to extract foreground and noises are removed based on the object motion. Kalman filter enables the prediction of the object position in the presence of partial occlusions, and changes the size of the animal detected automatically along the image sequence. The presented method is evaluated in various environments of unstable background because of winds, and illuminations changes. The results show that our approach is more robust to background noises and performs better than previous methods.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 추계종합학술대회 B
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• pp.157-160
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• 2008

A realization for moving object detecting and tracking system in two dimensional plane using stereo line CCDs and lighting source is presented in this paper. Instead of processing camera images directly, two line CCD sensor and input line image is used to measure two dimensional distance by comparing the brightness on line CCDs. The algorithms are used the moving object tracking and coordinate converting method. To ensure the effective detection of moving path, a detection algorithm to evaluate the reliability of each measured distance is developed. The realized system results are that the performance of moving object recognizing shows 5mm resolution and mean error is 1.89%, and enables to track a moving path of object per 100ms period.