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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Now showing 1 - 6 of 6

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.
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
;
交大名義發表
;
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.
Accelerating Vanishing Point-Based Line Sampling Scheme for Real-Time People Localization
(2017-03-01)
Liu, Chin-Wei
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Chen, Hua-Tsung
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Lo, Kuo-Hua
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Wang, Chih-Jung
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Chuang, Jen-Hui
In advanced video surveillance systems, people localization is usually a part of the complete system and should be accomplished in a short time so as to reserve sufficient processing time for subsequent high-level analysis, such as abnormal event/ behavior detection and intruder detection. Hence, in addition to localization accuracy, computational efficiency is of critical importance as well. In this paper, we adopt a vanishing point-based line sampling scheme and propose a fast multicamera people localization approach capable of locating a crowd of dense people and estimating their heights in a fairly short time with high accuracy. For each camera view, sample lines, originated from a vanishing point, of foreground objects are projected onto the ground plane. Then, people locations are estimated by detecting the ground regions containing a high density of the projected lines. Enhanced from some previous works, the proposed approach does not require processing steps of high computation cost, such as projecting all foreground pixels of all views to multiple reference planes or computing pairwise intersections of projected sample lines at different heights. In addition, some novel acceleration modules, such as torso validation and physical rule-based filtering, are developed to further reduce the computation time of people localization. The experiments on real surveillance scenes validate that the proposed approach achieves significant speedup (up to 186%) over state-of-the-art techniques while still ensure a comparably high localization accuracy, even for crowded scenes with serious occlusions.
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.
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
;
Lo, Kuo-Hua
;
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.

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