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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chuang, Jen-HuiHas files: Yes

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    A novel synchronous multi-intensity IR illuminator hardware implementation for nighttime surveillance
    (2015-01-01)
    Teng, Wen-Chih
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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
    For nighttime surveillance, it's difficult for an ordinary IR illuminator (which has a fixed light intensity) to generate a one-size-fits-all light condition for objects at different distances. Often, we will get overexposed (underexposed) image for an object which is too close to (too far from) the illuminator/camera in the nighttime. To partly resolve such problems, a multi-intensity IR illuminator was proposed which can emit periodically varying intensities of IR light toward an object. However, two major problems remain to be resolved when using such an illuminator, i.e., the "synchronization issue" for background modeling and the "viewing issue" for manual monitoring. In this paper, we propose a novel synchronous multi-intensity infrared (SMIIR) illuminator hardware design to help address these two issues. Hopefully this innovation will open the door to a new era of nighttime surveillance.
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    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.
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    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
    ;
    資訊工程學系
    ;
    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.
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    Recognizing jump patterns with physics-based validation in human moving trajectory
    (2013-10-01)
    Chen, Hua-Tsung
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    Ma, Kuo-Lian
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    Chuang, Jen-Hui
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    Lin, Horng-Horng
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    資訊工程學系
    This paper presents an approach to recognize jump patterns in human moving trajectory, differentiating jump tracks from planar moving tracks. Since human moving trajectory is one of the most informative representations for content understanding and event detection, trajectory-based video analysis has been gaining popularity. However, a jump action typically leads to violent change in human moving trajectory, since the person suddenly leaves the original plane on which he/she has been moving. The abnormal tracks of the trajectory would influence the performance of trajectory-based video analysis. Hence, differentiating jump tracks from planar moving tracks is of vital importance, not to mention that jump actions typically imply significant events, especially in sports games. In this paper, volleyball videos are used as case study to demonstrate the effectiveness of our proposed jump pattern recognition approach. We derive player trajectory by head tracking, analyze the movement of each player, and recognize potential jump tracks in player trajectories based on two important characteristics: (1) jumps cause pulse-like tracks in the trajectory and (2) the extensions of such tracks go through the vanishing point of vertical lines in the scenes. Finally, the jump positions/heights are estimated, in addition to the planar moving trajectory of each player on the court ground. The experiments show that satisfactory results can be obtained with the proposed recognition scheme. (C) 2013 Elsevier Inc. All rights reserved.
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    Vanishing Point-based Line Sampling for Real-time People Localization
    (2013-07-01)
    Lo, Kuo-Hua
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    Chuang, Jen-Hui
    ;
    資訊工程學系
    ;
    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.
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    An alternate priority planning algorithm for dual-arm systems
    (2006)
    Chuang, Jen-Hui
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    Lin, Chien-Chou
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    Chou, Tsun-Hou
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    資訊工程學系
    ;
    Department of Computer Science
    An alternate priority path planning algorithm for dual-manipulator systems is proposed in this paper. A master-slave architecture is used to deal with the coordination of two manipulators by alternately identifying configurations of the two manipulators. The proposed method utilizes a generalized potential field to evaluate repulsion between manipulators and obstacles in a workspace, so collision avoidance of the planned path can be guaranteed. The simulation results show that proposed algorithm is efficient, even in a narrow passage.
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    Regularized Background Adaptation: A Novel Learning Rate Control Scheme for Gaussian Mixture Modeling
    (2011-03-01)
    Lin, Horng-Horng
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    Chuang, Jen-Hui
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    Liu, Tyng-Luh
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    資訊工程學系
    ;
    Department of Computer Science
    To model a scene for background subtraction, Gaussian mixture modeling (GMM) is a popular choice for its capability of adaptation to background variations. However, GMM often suffers from a tradeoff between robustness to background changes and sensitivity to foreground abnormalities and is inefficient in managing the tradeoff for various surveillance scenarios. By reviewing the formulations of GMM, we identify that such a tradeoff can be easily controlled by adaptive adjustments of the GMM's learning rates for image pixels at different locations and of distinct properties. A new rate control scheme based on high-level feedback is then developed to provide better regularization of background adaptation for GMM and to help resolving the tradeoff. Additionally, to handle lighting variations that change too fast to be caught by GMM, a heuristic rooting in frame difference is proposed to assist the proposed rate control scheme for reducing false foreground alarms. Experiments show the proposed learning rate control scheme, together with the heuristic for adaptation of over-quick lighting change, gives better performance than conventional GMM approaches.
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    Learning a Scene Background Model via Classification
    (2009-05-01)
    Lin, Horng-Horng
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    Liu, Tyng-Luh
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    Chuang, Jen-Hui
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Learning to efficiently construct a scene background model is crucial for tracking techniques relying on background subtraction. Our proposed method is motivated by criteria leading to what a general and reasonable background model should be, and realized by a practical classification technique. Specifically, we consider a two-level approximation scheme that elegantly combines the bottom-up and top-down information for deriving a background model in real time. The key idea of our approach is simple but effective: If a classifier can be used to determine which image blocks are part of the background, its outcomes can help to carry out appropriate blockwise updates in learning such a model. The quality of the solution is further improved by global validations of the local updates to maintain the interblock consistency. And a complete background model can then be obtained based on a measurement of model completion. To demonstrate the effectiveness of our method, various experimental results and comparisons are included.
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    Drawing graphs with nonuniform nodes using potential fields
    (2009-12-01)
    Lin, Chun-Cheng
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    Yen, Hsu-Chun
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    Chuang, Jen-Hui
    ;
    資訊工程學系
    ;
    Department of Computer Science
    Graphs with nonuniform nodes arise naturally in many real-world applications. Although graph drawing has been a very active research in the computer science community during the past decade, most of the graph drawing algorithms developed thus far have been designed for graphs whose nodes are represented as single points. As a result, it is of importance to develop drawing methods for graphs whose nodes are of different sizes and shapes, in order to meet the need of real-world applications. To this end, a potential field approach, coupled with an idea commonly found in force-directed methods, is proposed in this paper for drawing graphs with nonuniform nodes in 2-D and 3-D. In our framework, nonuniform nodes are uniformly charged, while edges are modelled by springs. Using certain techniques developed in the field of potential-based path planning, we are able to find analytically tractable procedures for computing the repulsive force and torque of a node in the potential field induced by the remaining nodes. The crucial feature of our approach is that the rotation of every nonuniform node and the multiple edges between two nonuniform nodes are taken into account. In comparison with the existing algorithms found in the literature, our experimental results suggest this new approach to be promising, as drawings of good quality for a variety of moderate-sized graphs in 2-D and 3-D can be produced reasonably efficiently. That is, our approach is suitable for moderate-sized interactive graphs or larger-sized static graphs. Furthermore, to illustrate the usefulness of our new drawing method for graphs with zero-sized nodes, we give an application to the visualization of hierarchical clustered graphs, for which our method offers a very efficient solution. (C) 2009 Elsevier Ltd. All rights reserved.
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    A POTENTIAL-BASED PATH PLANNING ALGORITHM FOR HYPER-REDUNDANT MANIPULATORS
    (2010-04-01)
    Lin, Chien-Chou
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    Chuang, Jen-Hui
    ;
    資訊工程學系
    ;
    Department of Computer Science
    A novel collision avoidance algorithm based on the generalized potential model is proposed to solve the path-planning problem of hyper-redundant manipulators in 3D workspace. The approach computes repulsive force and torque between manipulator and obstacles by using the workspace information directly. A collision-free path for a manipulator can then be obtained by locally adjusting the manipulator configuration to search for minimum potential configurations using these forces and torques. The proposed approach is efficient since these potential gradients are analytically tractable. Furthermore, the proposed algorithm is also extended to dual-arm systems. Simulation results show that the proposed algorithm works well, in terms of computation time and collision avoidance.