Browsing by Author "Lin, Yen-Shuo"

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1-D Integral Image for Enhancing Efficiency and Effectiveness of Probabilistic Occupancy Map-Based People Localization Approach
(2016)
Lin, Yen-Shuo
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Chen, Hua-Tsung
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Hwang, Jenq-Neng
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Hsiao, Ching-Ju
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Chuang, Jen-Hui
The popularity of vision-based surveillance systems arouses much research attention in improving the accuracy and efficiency of people localization. Using probabilistic occupancy map (POM) becomes one of the mainstream approaches to people localization due to its great localization accuracy under severe occlusions and lighting changes. However, to enable the usage of rectangular human models and the subsequent 2-D integral image computation, it is assumed that videos are taken at head or eye level. Even so, the computation complexity is still high. Moreover, surveillance videos are often taken from security cameras located at a higher-up location with an oblique viewing angle, so that human models may be quadrilateral and the pixel-based 2-D integral image cannot be utilized. Accordingly, we propose the use of 1-D integral images which are produced for foreground object(s) in an image along equally-spaced line samples originated from the vanishing point of vertical lines (VPVL). Experimental results show that the proposed approach does improve the efficiency and effectiveness of the POM approach in more general camera configurations.
An Efficient Probabilistic Occupancy Map-Based People Localization Approach
(2015-01-01)
Lin, Yen-Shuo
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Chen, Hua-Tsung
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Chuang, Jen-Hui
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交大名義發表
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資訊工程學系
The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy.
Camera Self-Calibration from Tracking of Moving Persons
(2016-01-01)
Tang, Zheng
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Lin, Yen-Shuo
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Lee, Kuan-Hui
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Hwang, Jenq-Neng
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Chuang, Jen-Hui
In a video surveillance system with a single static camera, tracking results of moving persons can be effectively used for camera self-calibration. However, the current methods need to depend on robustness of both tracking and segmentation procedures. RANSAC has been widely used to remove outliers in finding the vertical vanishing point and the horizon line, but the performance is degraded when the proportion of outliers is high. Last but not least, all of them require excessive simplifications in the algorithmic procedures resulting in increasing reprojection error. In this paper, a robust segmentation and tracking system is applied to provide accurate estimation of head and foot locations of moving persons. The noise in the computation of vanishing points is handled by mean shift clustering and Laplace linear regression through convex optimization. We also propose to use the estimation of distribution algorithm (EDA) to search for the local optimal solution for camera calibration that minimizes average reprojection error on the ground plane, while relaxing the assumptions on camera parameters. Promising evaluations of the performance of our proposed method on real scenes are presented.
ESTHER: Joint Camera Self-Calibration and Automatic Radial Distortion Correction From Tracking of Walking Humans
(2019-01-01)
Tang, Zheng
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Lin, Yen-Shuo
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Lee, Kuan-Hui
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Hwang, Jenq-Neng
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Chuang, Jen-Hui
Camera calibration and radial distortion correction are the crucial prerequisites for many applications in image big data and computer vision. Many existing works rely on the Manhattan world assumption to estimate the camera parameters automatically; however, they may perform poorly when there was lack of man-made structure in the scene. As walking humans are the common objects in video surveillance, they have also been used for camera self-calibration, but the main challenges include the noise reduction for the estimation of vanishing points, the relaxation of assumptions on unknown camera parameters, and the radial distortion correction. In this paper, we present a novel framework for camera self-calibration and automatic radial distortion correction. Our approach starts with the reliable human body segmentation that is facilitated by robust object tracking. Mean shift clustering and Laplace linear regression are, respectively, introduced in the estimation of the vertical vanishing point and the horizon line. The estimation of distribution algorithm, an evolutionary optimization scheme, is then utilized to optimize the camera parameters and the distortion coefficients, in which all the unknowns in camera projection can be fine-tuned simultaneously. Experiments on the three public benchmarks and our own captured dataset demonstrate the robustness of the proposed method. The superiority of this algorithm is also verified by the capability of reliably converting 2D object tracking into 3D space.
Monitoring Debris Flows Using Spatial Filtering and Entropy Determination Approaches
(2013-10-01)
Kao, Hung-Ming
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Ren, Hsuan
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Lee, Chao-Shing
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Chen, Yen-Liang
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Lin, Yen-Shuo
We developed an automatic debris flow warning system in this study. The system uses a fixed video camera mounted over mountainous streams with a high risk for debris flows. The focus of this study is to develop an automatic algorithm for detecting debris flows with a low computational effort which can facilitate real-time implementation. The algorithm is based on a moving object detection technique to detect debris flow by comparing among video frames. Background subtraction is the kernel of the algorithm to reduce the computational effort, but non-rigid properties and color similarity of the object and the background color introduces some difficulties. Therefore, we used several spatial filtering approaches to increase the performance of the background subtraction. To increase the accuracy entropy is used with histogram analysis to identify whether a debris flow occurred. The modified background subtraction approach using spatial filtering and entropy determination is adopted to overcome the error in moving detection caused by non-rigid and similarities in color properties. The results of this study show that the approach described here can improve performance and also reduce the computational effort.
MULTIPLE-KERNEL ADAPTIVE SEGMENTATION AND TRACKING (MAST) FOR ROBUST OBJECT TRACKING
(2016)
Tang, Zheng
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Hwang, Jenq-Neng
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Lin, Yen-Shuo
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Chuang, Jen-Hui
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資訊工程學系
In a video surveillance system with static cameras, object segmentation often fails when part of the object has similar color with the background, resulting in poor performance of the subsequent object tracking. Multiple kernels have been utilized in object tracking to deal with occlusion, but the performance still highly depends on segmentation. This paper presents an innovative system, named Multiple-kernel Adaptive Segmentation and Tracking (MAST), which dynamically controls the decision thresholds of background subtraction and shadow removal around the adaptive kernel regions based on the preliminary tracking results. Then the objects are tracked for the second time according to the adaptively segmented foreground. Evaluations of both segmentation and tracking on benchmark datasets and our own recorded video sequences demonstrate that the proposed method can successfully track objects in similar-color background and/or shadow areas with favorable segmentation performance.
Vanishing Point-Based Image Transforms for Enhancement of Probabilistic Occupancy Map-Based People Localization
(2014-12-01)
Lin, Yen-Shuo
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Lo, Kuo-Hua
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Chen, Hua-Tsung
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Chuang, Jen-Hui
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資訊工程學系
The widespread use of vision-based surveillance systems has inspired many research efforts on people localization. In this paper, a series of novel image transforms based on the vanishing point of vertical lines is proposed for enhancement of the probabilistic occupancy map (POM)-based people localization scheme. Utilizing the characteristic that the extensions of vertical lines intersect at a vanishing point, the proposed transforms, based on image or ground plane coordinate system, aims at producing transformed images wherein each standing/walking person will have an upright appearance. Thus, the degradation in localization accuracy due to the deviation of camera configuration constraint specified can be alleviated, while the computation efficiency resulted from the applicability of integral image can be retained. Experimental results show that significant improvement in POM-based people localization for more general camera configurations can indeed be achieved with the proposed image transforms.
VP-TRANSFORM: A NOVEL VANISHING POINT-BASED IMAGE TRANSFORM FOR ENHANCEMENT OF PEOPLE LOCALIZATION
(2013-01-01)
Lin, Yen-Shuo
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Lo, Kuo-Hua
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Chen, Hua-Tsung
;
Chuang, Jen-Hui
;
資訊工程學系
The widespread use of vision-based surveillance systems has inspires many research efforts on people localization. In this paper, a novel nonlinear image transform based on the vanishing point of vertical lines, entitled VP-transform, is proposed for enhancement of the people localization scheme proposed in [1]. Utilizing the characteristic that the extensions of vertical lines intersect at a vanishing point, the proposed transformation aims at producing rectified images wherein each standing/walking person will have an upright appearance. Thus, the degradation in localization accuracy, due to deviation of the constraint of camera configuration specified in [1], can be alleviated. Experimental results validate the effectiveness of the proposed image transform that significant improvement in people localization can be achieved.