Browsing by Author "Lo, Kuo-Hua"

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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.
Acceleration of Vanishing Point-Based Line Sampling Scheme for People Localization and Height Estimation via 3D Line Sampling
(2012-01-01)
Lo, Kuo-Hua
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Wang, Chih-Jung
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Chuang, Jen-Hui
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Chen, Hua-Tsung
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資訊工程學系
With the popularity of vision-based camera surveillance, the research on people localization appeals to much attention. In this paper, we propose an efficient and effective system capable of locating a crowd of dense people in real time, using multiple cameras. For each camera view, sample lines, originated from a vanishing point, of foreground objects are projected on the ground plane. Ground regions containing a high density of projected lines are then used to find people locations. Enhanced from previous works, the people localization approach proposed in this paper needs not project all foreground pixels of all views to multiple reference planes or compute pairwise intersections of projected sample lines at different heights, resulting in significant improvement in computational efficiency. Furthermore, the people heights can also be estimated. Experimental results on real surveillance scenes show that comparable accuracy in people localization can be achieved with five times in computing speed compared with our previous approach.
Efficient Error Analysis of a Real-Time Vision-Based Pointing Systems
(2014-01-01)
Lo, Kuo-Hua
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Chuang, Jen-Hui
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Chen, Hua-Tsung
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資訊工程學系
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Department of Computer Science
In this paper, an efficient error analysis of a real-time vision-based pointing system is proposed. We use two cameras to implement the pointing system according to a simplified 3D reconstruction scheme which is based on image feature extraction, homography, and 3D geometry. To that end, we study the relationship between image noises and the ultimate reconstruction errors, and develop efficient methods to find the error range of the latter given a range of the former. Experimental results show that the proposed approach can find the error range satisfactorily. Accordingly, users of similar pointing systems can get more robust pointing results by selecting a special pointer location, or possibly a special pair of cameras, that will result in minimal range of pointing error.
Human Activity Analysis Based on a Torso-less Representation
(2008)
Chuang, Jen-Hui
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Lee, Chun-Wei
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Lo, Kuo-Hua
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資訊工程學系
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Department of Computer Science
Human activity recognition is a popular topic in the field of computer vision. While most analysis algorithms take into consideration of the whole human body, the movements of merely the head and limbs are often informative enough in many practical applications. In this paper, a novel approach is proposed to track these extruding parts of a human body in consecutive images. Accordingly, a simplified torso-less pattern of gesture is proposed to represent human activities, and with its effectiveness verified subjectively by some experimental results. Such a representation can not only ensure the privacy of the person being watched, but is also suitable for real-time surveillance based on bandwidth-limited communication since only a very small amount of data are used compared to conventional approaches.
INCORPORATING TEXTURE INFORMATION INTO REGION-BASED UNSUPERVISED IMAGE SEGMENTATION USING TEXTURAL SUPERPIXELS
(2014)
Hsu, Chih-Yu
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Hsieh, Yi-Yu
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Lo, Kuo-Hua
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Chuang, Jen-Hui
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資訊工程學系
Recently, an unsupervised image segmentation framework, Segmentation by Aggregating Superpixels (SAS) is proposed and shown to be very promising. However, the texture cues, which have been shown to be very effective in many researches, are not used. In this paper, we propose an effective method for incorporating texture information into the SAS framework, using superpixels. To extract texture information, our algorithm first uses texture filtering and subsequently GMM clustering. Then, we develop an edge-aware low-pass filtering to generate multiple-scale textural superpixels (TXSPs) from the clustering results. Finally, by joining TXSPs with the superpixel set originally used in SAS, the incorporation of texture information is accomplished. Our method achieves superior performance on the Berkeley Segmentation Dataset (BSDS300) under several evaluation criteria when compared to other benchmark algorithms.
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.
VANISHING POINT-BASED LINE SAMPLING FOR EFFICIENT AXIS-BASED PEOPLE LOCALIZATION
(2011)
Lo, Kuo-Hua
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Chuang, Jen-Hui
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資訊工程學系
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Department of Computer Science
In this paper, we propose an efficient people localization approach using multiple cameras based on axial representations of foreground regions. Unlike many previous methods that need to project all foreground pixels of all views to multiple reference planes via homography, we instead apply vanishing point-based line sampling to reduce the large amount of pixel processing so that computational efficiency can be greatly enhanced. Experimental simulations show that the proposed approach is more than 40 times faster than the compared, pixel-based localization method on average, without sacrificing the localization accuracy.
Vanishing Point-based Line Sampling for Real-time People Localization
(2013-07-01)
Lo, Kuo-Hua
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Chuang, Jen-Hui
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資訊工程學系
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Department of Computer Science
In this paper, we propose a real-time multicamera people localization method based on line sampling of image foregrounds. For each view, these line samples are originated from the vanishing point of lines perpendicular to the ground plane. With these line samples, vertical line samples in the 3-D scene can be reconstructed for potential human locations. After some efficient geometric refinement and filtering procedures, the remaining qualified 3-D line samples are clustered and integrated for the identification of locations and heights of people in the scene. Both indoor and outdoor scenarios are examined to demonstrate the effectiveness of our approach in handling serious occlusion in crowed scenes. The average localization error of less than 15 cm for average viewing distance of 15m suggests that our method can be applied to a broad range of surveillance applications that require the real-time computation of localization without using special hardware for acceleration.
VIEW-INVARIANT MEASURE OF LINE CORRESPONDENCE AND ITS APPLICATION IN PEOPLE LOCALIZATION
(2012)
Lo, Kuo-Hua
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Chuang, Jen-Hui
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資訊工程學系
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Department of Computer Science
A correspondence measure of 2D line segments in two different views is proposed in this paper. Such a quantitative measure is view-invariant and can handle line segment of arbitrary configuration in the 3D scene. A line-based people localization scheme is proposed by applying such a measure to improve the efficiency of [1]. By verifying whether 2D line samples from different views belong to the same person, computations associated with incorrectly reconstructed 3D line samples of people can be avoided. Experimental results show that people localization results, in terms of correctness and accuracy, comparable to [1] can be obtained with the proposed scheme, but with more than three times in computation speed.
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
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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 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.