• ETRI Journal
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• 제43권1호
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• pp.74-81
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• 2021

The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.912-928
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• 2019

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 춘계학술대회
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• pp.151-152
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• 2012

본 논문은 지역의 의사 결정 트리와 클러스터링을 사용하여 장면 전환 검출 방법을 제시한다. 지역 의사 결정 트리는 클러스터 경계선 검출 장면과 그 인접 프레임의 사이의 차이 값을 시간 유사분포를 비교하기 위해 같은 방식으로 검출하고, 그리고 클러스터 단위로 차이 값의 유사성과 그룹 프레임의 끊어지지 않는 시퀀스를 감지한다.

• 한국정보통신학회논문지
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• 제16권6호
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• pp.1199-1203
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• 2012

본 논문은 지역의 의사 결정 트리와 클러스터링을 사용하여 장면 전환 검출 방법을 제시한다. 지역 의사 결정 트리는 클러스터 경계 검출 장면과 그 인접 프레임의 사이의 차이 값을 시간 유사 분포를 비교하기 위해 같은 방식으로 검출하고, 그리고 클러스터 단위로 차이 값의 유사성과 그룹 프레임의 끊어지지 않는 시퀀스를 감지한다.

• 한국멀티미디어학회논문지
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• 제13권8호
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• pp.1128-1141
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• 2010

본 논문은 궤적을 군집화하여 혼잡한 영상에서 이동 객체를 검출하는 방법을 제안한다. 제안하는 방법은 객체의 외형 정보에 기반한 기존의 방법들과는 달리 객체의 움직임 정보만을 이용해 이동 객체를 검출한다. 이를 위하여 입력 영상의 매 프레임에서 특징점을 추출하며, 인접한 프레임간의 추적 과정을 통하여 특징점들의 궤적을 생성한다. 동일 객체에서 얻어진 궤적들은 유사한 움직임을 보일 것이라는 가정 하에 군집화 과정을 통하여 이동 객체를 검출한다. 궤적들의 군집화를 위하여 특징점 간의 위치, 움직임, 연속성에 기반한 에너지 함수로 궤적 간 유사도를 측정하였으며, conditional random fields (CRFs)를 이용하여 최적의 군집을 결정하였다. 기존의 궤적 군집화를 통한 이동 객체 검출 방법이 군집화 과정에서 한번 잘못 분류된 궤적은 잘못된 결과를 생성하는 것과는 달리, 제안한 방법에서는 군집화가 CRFs 상에서 에너지 최소화에 의해 수행되기 때문에 잘못 분류된 궤적이 반복 과정에서 다시 올바른 군집으로 재배열되는 것이 가능하다. 제안한 방법의 성능 측정을 위하여 서로 다른 혼잡도를 가지는 세 개의 영상을 이용하였으며, 약 94%의 검출률과 7%의 허위 경보율을 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권12호
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• pp.3315-3337
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• 2012

Crowded scenes analysis is a challenging topic in computer vision field. How to detect diverse motion patterns in crowded scenarios from videos is the critical yet hard part of this problem. In this paper, we propose a novel approach to mining motion patterns by utilizing motion information during both long-term period and short interval simultaneously. To capture long-term motions effectively, we introduce Motion History Image (MHI) representation to access to the global perspective about the crowd motion. The combination of MHI and optical flow, which is used to get instant motion information, gives rise to discriminative spatial-temporal motion features. Benefitting from the robustness and efficiency of the novel motion representation, the following motion pattern mining is implemented in a completely unsupervised way. The motion vectors are clustered hierarchically through automatic hierarchical clustering algorithm building on the basis of graphic model. This method overcomes the instability of optical flow in dealing with time continuity in crowded scenes. The results of clustering reveal the situations of motion pattern distribution in current crowded videos. To validate the performance of the proposed approach, we conduct experimental evaluations on some challenging videos including vehicles and pedestrians. The reliable detection results demonstrate the effectiveness of our approach.

• 대한기계학회논문집
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• 제17권11호
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• pp.2711-2722
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• 1993

Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.164-169
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• 2009

As one of the most interesting scenes, landmarks constitute a large percentage of the vast amount of scene images available on the web. On the other hand, a specific "landmark" usually has some characteristics that distinguish it from surrounding scenes and other landmarks. These two observations make the task of accurately estimating geographic information from a landmark image necessary and feasible. In this paper, we propose a method to identify landmark location by means of landmark recognition in view of significant viewpoint, illumination and temporal variations. We use GPS-based clustering to form groups for different landmarks in the image dataset. The images in each group rather fully express the possible views of the corresponding landmark. We then use a combination of edge and color histogram to match query to database images. Initial experiments with Zubud database and our collected landmark images show that is feasible.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3239-3267
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• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.671-674
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• 1988

This paper describes a matching method to solve the problem of occlusion in a two dimensional scene. The technique consist of three steps: generation of hypotheses, clustering of hypotheses by matching probability, updating of hypotheses. Using this algorithm, simulation results have been tested for 20 scenes contained the 80 models, and have obtained 95% of properly correct recognition rate in average.