Motion clustering-based action recognition technique using optical flow
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Abstract:
A new technique for action clustering-based human action representation on the basis of optical flow analysis and random sample consensus (RANSAC) method is proposed in this paper. The apparent motion of the human subject with respect to the background is detected using optical flow analysis, while the RANSAC algorithm is used to filter out unwanted interested points. From the remaining key interest points, the human subject is localized and the rectangular area surrounding the human body is segmented both horizontally and vertically. Next, the percentage of change of interest points at every small blocks at the intersections of horizontal and vertical segments from frame to frame are accumulated in matrix form for different persons performing the same action. An average of all these matrices is used as a feature vector for that particular action. In addition, the change in the position of the person along X-axis and Y-axis are cumulated for an action and included in the feature vectors. For the purpose of recognition using the extracted feature vectors, a distance-based similarity measure and a support vector machine (SVM)-based classifiers have been exploited. From extensive experimentations upon benchmark motion databases, it is found that the proposed method offers not only a very high degree of accuracy but also computational savings.Keywords:
RANSAC
Optical Flow
Feature (linguistics)
Benchmark (surveying)
Similarity measure
Similarity (geometry)
Position (finance)
Feature vector
Representation
최근 초고용량 무선 데이터 전송이 가능한 5G 기술이 등장하면서 360 VR 영상을 활용한 기술들이 주목 받고 있으며 이에 따른 이미지 정합에 대한 관심도 높아지고 있다. 본 논문에서는 RANSAC 알고리즘을 활용한 사용자 관심 영역의 매칭 알고리즘을 제안한다. 제안하는 매칭 방법은 RANSAC 알고리즘을 활용하여 사용자가 선택하는 관심 영역에 높은 가중치를 부여하여 이미지 정합을 수행하며, 특히 자연스러운 정합이 요구되는 영역에 대해 선택적으로 수행할 수 있다. 관심영역에 포함되는 대응점들에 가중치를 높게 설정하고 RANSAC 알고리즘의 샘플 선택 시 필수적으로 포함시키되, 몇 개의 특징점을 필수적으로 포함할 것인지에 따라 해당 특정 영역의 매칭 정도를 조절할 수 있다. 관심 영역 매칭 방법은 관심 영역을 설정하는 단계와 관심 영역 안의 대응점의 가중치를 높이는 단계, RANSAC 알고리즘을 활용하여 모델을 생성하고 그 모델을 사용하여 특징점의 Inlier와 Outlier를 설정하는 단계로 나뉘게 된다. 실험 결과, 선택된 관심영역의 대응점 위주로 매칭을 수행함으로써 사용자가 원하는 영역이 좀 더 현실과 유사한 결과를 얻을 수 있음을 확인하였다.
RANSAC
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증강 현실은 현실의 대상 위에 증강 객체를 표시하여 정보를 제공하는 것이 목적으로, 증강 객체의 좌표를 정확하게 계산하는 것이 핵심 기능이다. 증강 객체의 좌표를 계산하기 위해서는 두 이미지 간의 호모그래피 추정법을 이용하는데, 여기서 RANSAC(Random Sample Consensus)은 두 이미지에서 추출된 특징점 쌍 중에 적합한 4쌍을 선택하는 기능을 한다. 하지만 기존의 RANSAC의 경우 추출 과정에서 선택한 특징점의 배치가 두 이미지 간에서 기하학적으로 유사한지 보장할 수 없는 문제점이 존재한다. 본 논문에서는 이 문제점을 해결하기 위해 RANSAC에서 선택하는 특징점의 배치를 검사하는 방법을 제안한다. 제안하는 방법은 이미지 위에 특징점의 사각형을 그려서 정점의 순서와 내각의 분포를 각각 검사한다. 실험 결과 제안하는 알고리즘은 기존 RANSAC보다 결함률을 8.55% 줄였으며, 증강 객체를 보다 정확한 위치에 표시하였다. 우리는 제안하는 알고리즘을 통해 증강 현실에서 증강 객체 좌표의 정확도를 개선하였다.
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This paper proposes a novel approach towards human action recognition based on optical flow and random sample consensus (RANSAC) by utilizing frequency domain feature extraction. Action representations can be considered as image templates, which can be useful for understanding various actions or gestures as well as for recognition and analysis. Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene. Additionally, RANSAC is an iterative method to estimate parameters of a mathematical model from a set of observed data, which contains inliers and outliers. The proposed scheme employs optical flow to determine the motion of humans. Human motions are further localized and identified using RANSAC. Feature extraction for the purpose of action recognition is performed in frequency domain. It has been shown that the use of frequency domain features enhances the distinguishability of different actions and certain undesirable phenomena, such as camera movement and change in camera distance from the subject, are less severe in the frequency domain. Principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations have been carried out upon some standard motion databases. It is found that the proposed method offers not only computational savings but also a very high degree of accuracy.
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The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for optimizing the preview model parameters evaluation of RANSAC that has the benefit of offering fast and accurate RANSAC. With guaranteeing the same confidence of the solution as RANSAC, a very large number of erroneous model parameters obtained from the contaminated samples are discarded in the preview evaluation selection. And use local optimization step apply to selected models. The combination of these two strategies results in a robust estimation procedure that provides a significant speed and accuracy RANSAC techniques, while requiring no prior information to guide the sampling process.
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Many low- or middle-level three-dimensional reconstruction algorithms involve a robust estimation and selection step whereby parameters of the best model are estimated and inliers fitting this model are selected. The RANSAC (RANdom SAmple consensus) algorithm is the most widely used robust algorithm for this task. A new version of RANSAC, called distributed RANSAC (D-RANSAC), is proposed, to save computation time and improve accuracy. The authors compare their results with those of classical RANSAC and randomised RANSAC (R-RANSAC). Experiments show that D-RANSAC is superior to RANSAC and R-RANSAC in computational complexity and accuracy in most cases, particularly when the inlier proportion is below 65%.
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본 논문은 시점을 달리 하는 두 이미지 사이의 다중 호모그래피 관계를 RANSAC을 이용하여 동시에 추정하는 새로운 방안을 제안한다. 이상치가 많이 포함된 데이터에 대해서도 강건한 파라미터 추정이 가능한 RANSAC 알고리즘은 단일 모델에 대해서만 적용되는 제약을 가진다. 따라서, 이미지에 존재하는 여러 평면의 2D 투영 변환 관계들을 추정하기 위해서는 RANSAC 알고리즘을 순차적으로 수행해야 한다. 이 과정에서 데이터에 지속적으로 포함되는 이상치들은 모델 추정을 느리게 한다. 또한, 모델들은 적합치 비율에 의해 순차적으로 추정되기 때문에 알고리즘의 병렬화가 어렵다는 문제가 있다. 본 논문에서는 RANSAC 알고리즘의 수행 과정에서 찾아낸 부분적인 모델 관계를 이용하여 반복 시도 횟수를 줄이고 다중 호모그래피들을 동시에 추정할 수 있는 가이드된 순차 RANSAC 알고리즘을 제시한다. This study proposes a new method of multiple homographies estimation between two images. With a large proportion of outliers, RANSAC is a general and very successful robust parameter estimator. However it is limited by the assumption that a single model acounts for all of the data inliers. Therefore, it has been suggested to sequentially apply RANSAC to estimate multiple 2D projective transformations. In this case, because outliers stay in the correspondence data set through the estimation process sequentially, it tends to progress slowly for all models. And, it is difficult to parallelize the sequential process due to the estimation order by the number of inliers for each model. We introduce a guided sequential RANSAC algorithm, using the local model instances that have been obtained from RANSAC procedure, which is able to reduce the number of random samples and deal simultaneously with multiple models.
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본 논문은 패널 합착 후에 합착 상태를 검사하기 위한 패널 에지와 백라이트 모듈의 내측 에지 사이의 갭(Gap)을 측정 검사하는 방법을 제안하였다. 패널과 백라이트 모듈이 합착된 후, 합착 부분에 대해 영상을 촬영하면 패널 에지의 단차, 에지 오염, 백라이트 모듈의 소재 특성 및 변형으로 인한 영상 잡음이 발생된다. 영상 잡음으로 인하여 영상을 이용하기 보다는 3D 레이저 센서 장치를 이용하여 주로 측정되어지고 있다. 제안된 알고리즘은 표준 편차를 이용한 RANSAC(RANdom SAmple Consensus) 방법을 통해 에지 선을 검출하고 에지 선 간의 갭 거리를 측정하였다. 다양한 갭 영상에 대하여 기존의 RANSAC 방법과 비교하여 제안된 알고리즘의 성능을 평가하였다.
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To accelerate the RANSAC process for fundamental matrix estimation, two special modifications about RANSAC are proposed. Firstly, in the verification stage, not the correspondences are used to verify the hypothesis but the singular values of estimated fundamental matrix are directly used to evaluate the effectiveness of the matrix. Secondly, after getting a plausible estimation, the obvious outliers are eliminated from the correspondences set. This process can enhance the inliers' ratio in the remaining correspondences set, which will accelerate the sample progress. We call our method as outlier elimination based RANSAC (OE-RANSAC). Experimental results both from synthetic and real data have testified the efficiency of OE-RANSAC.
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Many low or middle level 3D reconstruction algorithms involve a robust estimation and selection step by which parameters of the best model are estimated and inliers fitting this model are selected. The RANSAC algorithm is the most widely used robust algorithm for this step. However, this robust algorithm is computationally demanding. A new version of RANSAC, called distributed RANSAC (D-RANSAC), is proposed in this paper to save computation time and improve accuracy. We compare our results with those of classical RANSAC and another state of the art version of it. Experiments show that D-RANSAC is superior to RANSAC in computational complexity and accuracy, and comparable with other proposed improved versions.
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Abstract The extraction of building roof planes from lidar data has become a popular research topic with random sample consensus ( RANSAC ) being one of the most commonly adopted algorithms. RANSAC extracts full planes, which is problematic when there are other points outside the plane boundary but within the plane space. This study proposes an improved RANSAC (I‐ RANSAC ) algorithm by removing points that do not belong to the roof plane. I‐ RANSAC selects a random point from the extracted roof plane and then searches for its neighbours within a given threshold to identify and remove outliers. The new algorithm was tested with 14 buildings from two datasets, where quality control measures showed significant improvement over standard RANSAC .
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