• International Journal of Advanced Culture Technology
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• 제4권4호
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• pp.81-86
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• 2016

In this paper, we propose a simple and effective vision-based tracking controller design for autonomous object tracking using multicopter. The multicopter based automatic tracking system usually unstable when the object moved so the tracking process can't define the object position location exactly that means when the object moves, the system can't track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control used to improve the stability of tracking system, so that the result object tracking became more stable than before, it can be seen from error of tracking. A computer vision and control strategy is applied to detect a diverse set of moving objects on Raspberry Pi based platform and Software defined PID controller design to control Yaw, Throttle, Pitch of the multicopter in real time. Finally based series of experiment results and concluded that the PID control make the tracking system become more stable in real time.

• Journal of Information Processing Systems
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• 제16권2호
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• pp.261-276
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• 2020

Digital contents services are one of the topics that have been intensively studied in the media industry, where various semantic and ontology techniques are applied. However, query execution for ontology data is still inefficient, lack of sufficient extensible definitions for node relationships, and there is no specific semantic method fit for media data representation. In order to make the machine understand digital contents (DCs) data well, we analyze DCs data, including static data and dynamic data, and use ontology to specify and classify objects and the events of the particular objects. Then the formal representation method is proposed which not only redefines DCs data based on the technology of OWL/RDF, but is also combined with media segmentation methods. At the same time, to speed up the access mechanism of DCs data stored under the persistent database, an ontology-based DCs query solution is proposed, which uses the specified distance vector associated to a surveillance of semantic label (annotation) to detect and track a moving or static object.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2009년도 춘계학술대회
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• pp.219-222
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• 2009

Rapid object recognition is one of the main stream research themes focusing to reveal how human recognizes object and interacts with environment in natural world. This field of study is of consequence in that it is highly important in evolutionary perspective to quickly see the external objects and judge their characteristics to plan future reactions. In this study, we investigated how human detect natural scene objects and categorize them in a limited time frame. We applied Magnetoencepahlogram (MEG) while participants were performing detection (e.g. object vs. texture) or basic-level categorization (e.g. cars vs. dogs) tasks to track the dynamic interaction in human brain for rapid object recognition process. The results revealed that detection and categorization involves different temporal and functional connections that correlated for the successful recognition process as a whole. These results imply that dynamics in the brain are important for our interaction with environment. The implication from this study can be further extended to investigate the effect of subconscious emotional factors on the dynamics of brain interactions during the rapid recognition process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• 공학논문집
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• 제6권2호
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• pp.109-117
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• 2004

본 논문에서는 스테레오 영상 시퀀스에서 스네이크와 변이 정보(disparity information)를 이용한 객체 윤곽 추적 알고리즘을 제안한다. 제안하는 방법은 두 단계로 구성된다. 첫 번째는 변이 공간(disparity space)에서 스네이크 포인트의 모션 정보를 구하여 후보 스네이크 포인트를 결정하고, 두 번째는 후보 스네이크 포인트에 새로 정의한 스네이크 에너지 함수를 적용하여 관심객체의 윤곽을 추적하는 과정으로 구성된다. 제안한 방법은 복잡한 배경에서도 관심객체의 윤곽을 추적할 수 있었고, 실험을 통해 성능을 분석하였다.

• 천문학회보
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• 제44권1호
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• pp.73.4-73.4
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• 2019

The TXS0506+056 is a blazar and counterpart of the neutrino event IceCube-170922A. It is the first time that the neutrino event and flaring event in electromagnetic wave (EM) coincided. We observed TXS0506+056 with medium-bands in optical using 0.25m and 2.1m telescope at McDonald observatory about a month after the neutrino event. We tracked the variability of SED of the target for three weeks, and our observation showed no abrupt variability in optical range during this period. We concluded that a month after the neutrino event, the TXS0506+056 became less active and shows no feature of the energetic event. We also concluded that the medium-bands are well suited for tracking SEDs of objects. Our result demonstrates the potential of the wide-field 0.25m telescope (5.5 deg^2) for finding transient objects and track the variability of sources like AGNs.

• 한국멀티미디어학회논문지
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• 제25권2호
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• pp.145-156
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• 2022

Nowadays, the complexity of object tracking models among hardware applications has become a more in-demand duty to complete in various indeterminable environment tracking situations with multifunctional algorithm skills. In this paper, we propose a virtual city environment using AirSim (Aerial Informatics and Robotics Simulation - AirSim, CityEnvironment) and use the DQN (Deep Q-Learning) model of deep reinforcement learning model in the virtual environment. The proposed object tracking DQN network observes the environment using a deep reinforcement learning model that receives continuous images taken by a virtual environment simulation system as input to control the operation of a virtual drone. The deep reinforcement learning model is pre-trained using various existing continuous image sets. Since the existing various continuous image sets are image data of real environments and objects, it is implemented in 3D to track virtual environments and moving objects in them.

• Journal of information and communication convergence engineering
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• 제21권3호
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• pp.252-260
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• 2023

A bullet launcher can be developed as a smart instrument, especially for use in the military section, that can track, identify, detect, mark, lock, and shoot a target by implementing an image-processing system. In this research, the application of object recognition system, laser encoding as a unique marker, 2-dimensional movement, and pneumatic as a shooter has been studied intensively. The results showed that object recognition system could detect various colors, patterns, sizes, and laser blinking. Measuring the average error value of the object distance by using the camera is ±4, ±5, and ±6% for circle, square and triangle form respectively. Meanwhile, the average accuracy of shots on objects is 95.24% and 85.71% in indoor and outdoor conditions respectively. Here, the average prototype response time is 1.11 s. Moreover, the highest accuracy rate of shooting results at 50 cm was obtained 98.32%.

• 사물인터넷융복합논문지
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• 제2권3호
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• pp.37-41
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• 2016

본 논문에서는 영상처리기술과 네트워크와 연동이 가능한 센서 기술을 활용하여 발빠른 첨단기술에 맞춘 사물 인터넷을 기반으로 한 가정 및 사업 환경을 구축하고자 하였다. OpenCV 라이브러리의 분석 알고리즘을 활용한 영상 처리 기술을 사용하여 객체를 탐지 및 추적하고 그에 대한 데이터로 객체를 추적하고 다양한 센서들을 제어한다. 또한, 마스터 싱글보드와 슬레이브 싱글보드를 통하여 다양한 센서들을 통제하고 센싱 네트워크 환경을 구축 및 연계하여 데이터를 수집하고 기록하여 다양한 서비스를 제공 할 수 있는 플랫폼을 구현하고자 한다.

• 한국통신학회논문지
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• 제36권8C호
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• pp.489-496
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• 2011

무선 센서 네트워크에서 넓은 지역의 현상 및 움직임을 지속적으로 추적, 관찰하기 위해 에너지 효율성은 가장 중요한 요소이다. 이러한 망에서 에너지를 절약하기 위해 센서노드들에 대한 선택적 활성화 기법은 효과적인 방법이다. 그러나 이러한 선택적 활성화 기법을 적용한 기존의 대부분의 연구들은 침입자, 탱크와 같은 개별객체에 대해서만 고려했기 때문에 산불, 유독가스와 같은 연속객체에 대해서는 기존 기법을 적용하기 어렵다. 이는 연속객체들은 주변 환경의 영향에 상당히 민감하기 때문에 매우 유동적이고 불안정하여 이동할 다음 위치를 단순히 시공간 기법만을 가지고 예상할 수 없지 때문이다. 따라서 본 논문에서는 망의 센서노드들이 충분히 밀집한 경우 보다 정밀한 예측을 위해 객체가 퍼지거나 수축될 것으로 예상되는 지역의 센서들을 클러스터 단위로 활성화 시키는 클러스터 기반의 선택적 활성화 기법을 제안한다. 또한, 본 방안은 예측을 위한 계산이 동시에 수행될 필요가 없기 때문에 예측을 함에 있어 비동기식 기법을 적용한다.