Browsing by Author "Lin, Horng-Horng"

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A pattern-based inter-/extra-polation approach for image scaling
(2007)
Chuang, Jen-Hui
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Lin, Horng-Horng
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Wu, Szu-Hui
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資訊工程學系
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Department of Computer Science
A novel approach to simultaneous scaling and enhancing of a digital image is proposed, where efficient code matching is utilized to classify various edge/corner patterns. Adaptive schemes of inter-/extra-polations are adopted to maintain a balance of smoothness and sharpness of the scaled image, with unlimited scaling factors. Unlike many complex methods for image scaling and enhancement, our approach is very simple, making hardware implementation feasible.
Efficient Background Modeling Using Nonparametric Histogramming
(2013-01-01)
Lin, Horng-Horng
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Shih, Li-Chen
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Chuang, Len-Hui
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資訊工程學系
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多媒體工程研究所
With rapid increase in the deployment of highdefinition surveillance cameras, the need of efficient video analytics for extracting video objects from high-resolution surveillance videos in real time has become more and more demanding. Conventional background modeling methods, e.g., the Gaussian mixture modeling (GMM), although having long been proven to be effective for foreground object extraction, are actually not efficient enough for the real-time analysis of high-resolution videos. We thus propose a novel background modeling approach using nonparametric histogramming that can derive a holistic, histogram-based background model for each pixel with low computational complexity. Due to the simple algorithm design, the proposed approach can be easily implemented by fixedpoint computation. Without using any accelerator (like CUDA, Intel SIMD, or Intel IPP library), multi-threading or sub-sampling technique, our implementation of the proposed algorithm achieves high efficiency for the processing of 1920x1080 color videos at similar to 18.81 fps on a general computer (Intel Core i7 3.4GHz CPU). In the experimental comparisons, the proposed approach is similar to 3.9 times faster than the GMM, while giving comparable foreground segmentation results.
Intelligent Nighttime Video Surveillance Using Multi-Intensity Infrared Illuminator
(2011-01-01)
Lu, Peggy Joy
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Chuang, Jen-Hui
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Lin, Horng-Horng
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資訊工程學系
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Department of Computer Science
In nighttime video surveillance, the image details of far objects are often hard to be identified due to poor illumination conditions while the image regions of near objects may be whitened due to overexposure. To alleviate the two problems simultaneously for nighttime video surveillance, we adopt a new multi-intensity infrared illuminator as a supportive light source to provide multiple illumination levels periodically. By using the illuminator with multiple degrees of illumination power, both far and near objects can be clearly captured. For automatic detection of foreground objects at different distances in the image sequences captured with the multi-intensity infrared illuminator, two foreground object detection methods are proposed in this paper. Experimental results show that the two methods both achieve >90% accuracy in average in foreground object detection while giving different computational complexities.
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
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資訊工程學系
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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.
Practical error analysis of cross-ratio-based planar localization
(2007)
Chuang, Jen-Hui
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Kao, Jau-Hong
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Lin, Horng-Horng
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Chin, Yu-Ting
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資訊工程學系
Recently, more and more computer vision researchers are paying attention to error analysis so as to fulfill various accuracy requirements arising from different applications. As a geometric invariant under projective transformations, cross-ratio is the basis of many recognition and reconstruction algorithms which are based on projective geometry. We propose an efficient way of analyzing localization error for computer vision systems which use cross-ratios in planar localization. By studying the inaccuracy associated with cross-ratio-based computations, we inspect the possibility of using linear transformation to approximate localization error due to 2-D noises of image extraction for reference points. Based on such a computationally efficient analysis, a practical way of choosing point features in an image so as to establish the probabilistically most accurate planar location system using cross-ratios is developed.
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.
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
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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.
Robust License Plate Detection in Nighttime Scenes using Multiple Intensity IR-Illuminator
(2012)
Chen, Yi-Ting
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Chuang, Jen-Hui
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Teng, Wen-Chih
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Lin, Horng-Horng
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Chen, Hua-Tsung
The functionality of video surveillance is significantly degraded by the low illumination and poor visibility under the nighttime environment. However, the demand for nighttime surveillance is no less than the daytime one because of the high incidence of accidents during night. The Infrared (IR) light source with fixed intensity works for only certain distance, resulting in the defect of underexposure/overexposure due to the object being too far from/close to the light source. In this paper an innovative idea is brought up that we use a multiple intensity IR-illuminator to enhance the effective distance of license plate detection. Based on the stroke width of the license ID, license plates are detected in the images under different illuminations and then the results are integrated into a synthesized high dynamic range image, in which the license plate regions and the background scene can be better visualized. Experimental results show that the proposed approach can effectively enlarge the monitored area in both depth and width, as well as enhance the security level of nighttime video surveillance.