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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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    Vanishing Point-Based Image Transforms for Enhancement of Probabilistic Occupancy Map-Based People Localization
    (2014-12-01)
    Lin, Yen-Shuo
    ;
    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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    k-Angle Object Coverage Problem in a Wireless Sensor Network
    (2012-12-01)
    Tseng, Yu-Chee
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    Chen, Po-Yu
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    Chen, Wen-Tsuen
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    資訊工程學系
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    Department of Computer Science
    One of the fundamental issues in sensor networks is the coverage problem, which reflects how well a field is monitored or tracked by sensors. Various versions of this problem have been studied, such as object, area, barrier, and hole coverage problems. In this paper, we define a new k-angle object coverage problem in a wireless sensor network. Each sensor can only cover a limited angle and range, but can freely rotate to any direction to cover a particular angle. Given a set of sensors and a set of objects at known locations, the goal is to use the least number of sensors to k-angle-cover the largest number of objects such that each object is monitored by at least k sensors satisfying some angle constraint. We propose centralized and distributed polynomial-time algorithms to solve this problem. Simulation results show that our algorithms can be effective in maximizing coverage of objects. A prototype system is developed to demonstrate the usefulness of angle coverage.
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    Real-Time Security Monitoring Around a Video Surveillance Vehicle With a Pair of Two-Camera Omni-Imaging Devices
    (2011-10-01)
    Yuan, Pei-Hsuan
    ;
    Yang, Kuo-Feng
    ;
    Tsai, Wen-Hsiang
    ;
    資訊工程學系
    ;
    Department of Computer Science
    A pair of two-camera omni-imaging devices is designed for use on the roof of a video surveillance vehicle, and corresponding 3-D vision-based techniques for real-time security surveillance around the vehicle are proposed, which may be used to monitor passing-by persons around the vehicle. First, the design of the pair of two-camera omni-imaging devices, each device consisting of two omnicameras, with their optical axes vertically aligned, is described. Then, a new analytic technique for fast 3-D space data acquisition that is based on a panomapping method for image-to-world space transformation, as well as the rotational invariance property of the omni-image, is proposed. Techniques for constructing top-and perspective-view images for the convenient observation of the monitored environment are also proposed. Finally, 3-D vision techniques for automatically detecting passing-by persons and computing their locations and body heights are proposed, followed by experimental results, which show the precision and feasibility of the proposed techniques.
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    A Spatial-Extended Background Model for Moving Blobs Extraction in Indoor Environments
    (2009-11-01)
    Zhao, San-Lung
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    Lee, Hsi-Jian
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    資訊工程學系
    ;
    Department of Computer Science
    This paper presents a system for extracting regions of moving objects from an image sequence. To segment the foreground regions of ego-motion objects, we create a background model and update it using recent background variations. Since background images are usually changed in blobs, spatial relations are used to represent background appearances, which may be affected drastically by illumination changes and background object motion. To model the spatial relations, the joint colors of each pixel-pair are modeled as a mixture of Gaussian (MoG) distributions. Since modeling the colors of all pixel-pairs is expensive, the colors of pixel-pairs in a short distance are modeled. The pixel-pairs with higher mutual information are selected to represent the spatial relations in the background model. Experimental results show that the proposed method can efficiently detect the moving object regions when the background scene changes or the object moves around a region. By comparing with Gaussian background model and the MoG-based model, the proposed method can extract object regions more completely.