• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.71-73
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• 2019

How much and where vehicles exist is an essential element in the implementation of a GeoAI-based urban environment that reflects traffic information. In this paper, we trained vehicle data using Mask R-CNN that deep learning model useful for object detection and extraction, and verified vehicle detection in actual aerial images taken with drones.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.498-500
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• 2012

비디오 해석 및 추적기술은 특정한 시스템에서만 적용되는 것이 아니다. 이것은 비디오 내에서 의미 있는 정보를 능동적으로 감시 대상을 정의, 해석, 모델화, 추정 및 추적 할 수 있는 기반 기술을 의미하다. 일반적으로 감시시스템에서 감시 대상은 사람이나 차량이며, 상황에 따라 출입통제 구역으로 설정하기도 한다. 이는 연속된 영상에서 객체의 형태, 모양, 행동 분석, 움직임, 색상정보를 가지고 데이터 정의, 검출, 모델화를 통하여 인식, 식별 그리고 추적한다. 본 논문에서는 비디오 영상분석을 통해 단일카메라기반의 감시시스템과 PTZ 카메라기반 감시시스템 제안한다. 이때 단일 카메라기반의 감시는 배경생성방법을 이용하여 연속된 영상내의 객체를 지속적으로 관리가 가능하도록 설계하였고, PTZ 카메라기반의 감시는 카메라의 이동에 따른 배경안정화 방법과 카메라의 절대좌표를 활용하여 카메라 이동을 제어함과 동시에 오검출 문제를 해결하였다. 실험 및 결과분석으로는 시나리오 환경에서 배경생성방법을 이용한 검출의 정확성과 PTZ카메라 위치 변화에도 강인한 검출 결과를 비교 분석하였다.

• Journal of IKEEE
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• v.7 no.2 s.13
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• pp.253-259
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• 2003

In a good 3D engine, objects interactions are similar to those of real-world. Collision is one of the interactions. It includes whether collision took place or not, where collision took placed, and reaction after collision took place. More precise collision detection needs more time. If there exist required precision, detection time can be controlled by choosing appropriate detection model. Therefore, we need a selection mechanism for the collision detection with respect to required precision and detection time. In this paper, a collision detection model with seven different precision levels is examined. And relationship between detection time and precision is analyzed. Consequently, we propose a selection mechanism for collision detection model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.956-962
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• 2018

As the study of the object feature detection from image, we explain methods to identify the species of the tree in forest using the picture taken from dron. Generally there are three kinds of methods, which are GLCM (Gray Level Co-occurrence Matrix) and Gabor filters, in order to extract the object features. We proposed the object extraction method using the statistical properties of trees in this research because of the similarity of the leaves. After we extract the sample images from the original images, we detect the objects using cross correlation techniques between the original image and sample images. Through this experiment, we realized the mean value and standard deviation of the sample images is very important factor to identify the object. The analysis of the color component of the RGB model and HSV model is also used to identify the object.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.16-22
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• 2011

This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.451-452
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• 2022

본 논문에서는 딥러닝 영상인식 기술을 활용한 객체검출 모델인 YOLOv4를 활용하여 탈모 자가 진단 앱을 제안한다. 본 논문에서 제안하는 앱은 실시간 영상처리기술인 YOLOv4를 사용하여 탈모 유무와 탈모 유형에 대해 학습을 하고, 앱에서 사용자가 자신의 이마 라인을 촬영하여 사진이 서버에 전송이 되고 서버에서 학습된 모델을 이용하여 검출된 탈모 유무 판단과 탈모 단계 판단의 결과값을 다시 앱으로 전송한다. 탈모에 대한 정확한 진단을 통하여 치료 방법과 치료 시기 결정에 대하여 도움을 줄 것으로 기대된다.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.2
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• pp.85-90
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• 2021

A ADAS(Advanced Driver Assistance System) for the safe driving is an important area in autonumous car. Specially, a ADAS software using an image sensors attached in previous car is low in building cost, and utilizes for various purpose. A algorithm for detecting the break-lamp from the tail-lamp of preceding vehicle is proposed in this paper. This method can perceive the driving condition of preceding vehicle. Proposed method uses the YOLO techinicque that has a excellent performance in object tracing from real scene, and extracts the intensity variable region of break-lamp from HSV image of detected vehicle ROI(Region Of Interest). After detecting the candidate region of break-lamp, each isolated region is labeled. The break-lamp region is detected finally by using the proposed selective-attention model that percieves the shape-similarity of labeled candidate region. In order to evaluate the performance of the preceding vehicle break-lamp detection system implemented in this paper, we applied our system to the various driving images. As a results, implemented system showed successful results.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.83-83
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• 1998