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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 - 9 of 9
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    A Web-based Camera Dispatch System for Video Surveillance with Dynamic Requirements
    (2015-01-01)
    Li, Kuan-Yi
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    Liang, Jia-Ming
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    Fan, Chung-Shuo
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    Tseng, Yu-Chee
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    Lin, Yi-Ting
    Video surveillance systems are commonly used to monitor environments, such as factories, shopping malls, offices, and schools, for safety and security. However, traditional closed-circuit television cameras can only capture static scenes. When unexpected events happen, such as fire accidents or stranger intrusion, the recorded video data cannot provide immediate and precise information. In this work, we design a camera dispatch system for video surveillance with pan-tilt-zoom (PTZ) cameras, which can monitor event-areas/targets real-timely and flexibly. Users can dynamically define the monitoring requirements, such as pixel-per-foot and viewing-angle of the event-areas/targets. Then, a dispatch algorithm will decide the most necessary cameras and corresponding settings to monitor the event-areas/targets immediately. The control panel is implemented as a web-based service. The demo shows that users can easily monitor potential event-areas/targets anywhere, anytime through a web browser.
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    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.
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    Block-based Feature Extraction Model for Early Fire Detection
    (2017-01-01)
    Chou, Kuang-Pen
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    Prasad, Mukesh
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    Gupta, Deepak
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    Sankar, Sharmi
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    Xu, Ting-Wei
    Every year the fire disaster always causes a lot of casualties and property damage. Many researchers are involved in the study of related disaster prevention. Early warning systems and stable fire can significantly reduce the damage caused by fire. Many existing image-based early warning systems can perform well in a particular field. In this paper, we propose a general framework that can be applied in most realistic environments. The proposed system is based on a block-based feature extraction method, which analyses local information in separate regions leading to a reduction in computing data. Local features of fire block are extracted from the detailed characteristics of fire objects, which include fire color, fire source immobility, and disorder. Each local feature has high detection rate and filter out different false-positive cases. Global analysis with fire texture and non-moving properties are applied to further reduce false alarm rate. The proposed system is composed of algorithms with low computation. Through a series of experiments, it can be observed that Experimental results show that the proposed system has higher detection rate and low false alarm rate under various environment.
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    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.
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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.
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    Automatic fall incident detection in compressed video for intelligent homecare
    (2007)
    Lin, Chia-Wen
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    Ling, Zhi-Hong
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    資訊工程學系
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    Department of Computer Science
    This paper presents a compressed-domain fall incident detection scheme for intelligent homecare applications. First, a compressed-domain object segmentation scheme is performed to extract moving objects based on global motion estimation and local motion clustering. After detecting the moving objects, three compressed-domain features of each object are then extracted for identifying and locating fall incidents. The proposed system can differentiate fall-down from squatting by taking into account the event duration. Our experiments show that the proposed method can correctly detect fall incidents in real time.
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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.
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    MULTI-CAMERA VEHICLE IDENTIFICATION IN TUNNEL SURVEILLANCE SYSTEM
    (2015)
    Chen, Hua-Tsung
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    Chu, Ming-Chu
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    Chou, Chien-Li
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    Lee, Suh-Yin
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    Lin, Bao-Shuh
    Tunnel traffic security has received increasing attention since accidents in tunnels may cause serious casualties. Surveillance cameras are widely equipped in tunnels for traffic condition monitoring and safety maintenance. Vehicle identification among multiple cameras is an essential component in tunnel surveillance systems. In this paper, we propose a Spatiotemporal Successive Dynamic Programming ((SDP)-D-2) algorithm for identifying vehicles between pairs of cameras. Taking color information into consideration, we extract features based on Harris corner detection with OpponentSIFT descriptors. "Tracking-by-identification" for vehicles across multiple cameras can thus be achieved. Extensive experiments on real tunnel video data show that the proposed (SDP)-D-2 algorithm outperforms state-of-the-art methods.
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    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.