Browsing by Author "Chuang, Jen-Hui"

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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.
A CONCURRENT PLANNING ALGORITHM FOR DUAL-ARM SYSTEMS
(2007)
Chuang, Jen-Hui
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Chan, Ting-Wei
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Lin, Chien-Chou
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資訊工程學系
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Department of Computer Science
An algorithm for path planning of object held by multiple manipulators is presented in this paper. The proposed path planning algorithm is composed with two planners: path planner of the object and motion planner of manipulators. Both planners use a generalized potential model to evaluate the repulsion between object/manipulators and obstacles, so as to avoid collision. By adding virtual obstacle planes to roughly divide the workspace into several subspaces, a concurrent approach of path planning is also proposed. With each subspace designated to a manipulator, collisions between manipulators can be avoided. Therefore, paths of all. manipulators can be planned at the same time.
A Fully Automatic Approach for Fisheye Camera Calibration
(2018-01-01)
Tai, Yen-Chou
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Hsieh, Yi-Yu
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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
An automatic calibration procedure for a fisheye camera is presented in this paper by employing a flat panel monitor. The procedure does not require precise camera-monitor alignment, and any manual input of data or commands, making it useful for factory automation for mass production of such cameras. The fully automatic calibration procedure, which requires the generation of various test patterns on the display, and analysis of fisheye images of these patterns, consists of the following steps: (i) estimate the image center of the camera, (ii) identify the line on the monitor which intersects optical axis of the camera perpendicularly, and (iii) along the above line, obtain calibration data needed in de-warping the fisheye image. Experimental results demonstrate that the proposed approach performs satisfactorily in terms of effectiveness and accuracy.
A Light Deep Learning Based Method for Bank Serial Number Recognition
(2018-01-01)
Umam, Ardian
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Chuang, Jen-Hui
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Li, Dong-Lin
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資訊工程學系
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電機工程學系
A full stage bank serial number (SN) recognition system is proposed in this paper. We introduce Block-wise Prediction Networks (BPN) to treat the localization of an SN as block-wise binary classification, which can be considered as a coarse version of dense/pixel-wise prediction used in semantic segmentation. The benefits include short execution time, which is equal to 85.22 ms in CPU, and the use of global features instead of local features to improve the segmentation. Our system then separates the localized Region of Interest (Rol) into individual characters, and feeds them into softmax CNN classifier. Experimental results show that the proposed method can achieve 99.92% and 99.24% accuracy for character and SN of Renminbi (RMB), respectively, tested with 2,368 two sides images of 1,184 RMB bills.
A Novel Egocentric Pointing System Based on Smart Glasses
(2017-01-01)
Hsieh, Yi-Yu
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Wei, Yu-Han
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Chen, Kuan-Wen
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Chuang, Jen-Hui
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資訊工程學系
In this paper, we propose a novel, egocentric pointing system based on Google Glass which is equipped with an optical head-mounted display (OHMD) and a near-eye camera, with the eye-pointing line passing through the lower left corner of the display. For a pointed target, the pointing (or ranging) algorithm is based on a distance-pixel curve established from the camera-eye (epipolar) geometry. Additional pointing algorithms for estimating gazing point on a planar surface are also developed by establishing another distance-pixel curve along the same epipolar line. Experiments show that less than 0.32 degrees angular error in the egocentric pointing can be achieved for object distance ranging from 80cm to 178cm by the best estimation scheme, with slightly less accurate results (i.e. 0.58 degrees) achievable by simpler estimation schemes.
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.
A NOVEL VIDEO SUMMARIZATION METHOD FOR MULTI-INTENSITY ILLUMINATED INFRARED VIDEOS
(2013-01-01)
Chuang, Jen-Hui
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Tsai, Wen-Jing
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Chan, Chia-Hsin
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Teng, Wen-Chih
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Lu, I-Chun
In nighttime video surveillance, proper illumination plays a key role for the image quality. For ordinary IR-illuminators with fixed intensity, faraway objects are often hard to identify due to insufficient illumination while nearby objects may suffer from over-exposure, resulting in image foreground/background of poor quality. In this paper we proposed a novel video summarization method which utilizes a novel multi-intensity IR-illuminator to generate images of human activities with different illumination levels. By adopting GMM-based foreground extraction procedure for images acquired for each illumination level, foreground objects with most plausible quality can be selected and merged with a preselected representation for still background. The result brings out a reasonable video summary for moving foreground, which is generally unachievable for nighttime surveillance videos.
A Path Planning Algorithm using Generalized Potential Model for Hyper-Redundant Robots with 2-DOF Joints
(2011-06-01)
Lin, Chien-Chou
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Chuang, Jen-Hui
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Hsieh, Cheng-Tieng
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資訊工程學系
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Department of Computer Science
This paper proposes a potential-based path planning algorithm of articulated robots with 2-DOF joints. The algorithm is an extension of a previous algorithm developed for 3-DOF joints. While 3-DOF joints result in a more straightforward potential minimization algorithm, 2-DOF joints are obviously more practical for active operations. The proposed approach computes repulsive force and torque between charged objects by using generalized potential model. A collision-free path can be obtained by locally adjusting the robot configuration to search for minimum potential configurations using these force and torque. The optimization of path safeness, through the innovative potential minimization algorithm, makes the proposed approach unique. In order to speedup the computation, a sequential planning strategy is adopted. Simulation results show that the proposed algorithm works well compared with 3-DOF-joint algorithm, in terms of collision avoidance and computation efficiency.
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.
A POTENTIAL-BASED PATH PLANNING ALGORITHM FOR HYPER-REDUNDANT MANIPULATORS
(2010-04-01)
Lin, Chien-Chou
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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 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.
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.
Alpha matting using robust color sampling and fully connected conditional random fields
(2018-06-01)
Lin, Fang-Ju
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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
Alpha matting refers to the problem of softly extracting the foreground from a given image. Previous matting approaches often focused on using na < ve color sampling methods to estimate foreground and background colors for unknown pixels. Existing sampling-based matting methods often collect samples only near the unknown pixels, which may yield poor results if the true foreground and background samples are not found. In this paper, we present novel approach to extract foreground elements from an image through color and opacity (i.e., alpha) estimations, which consider available samples in a search window of variable size for each unknown pixel. Our proposed sampling method is robust in that similar sampling results can be generated for input trimaps of different unknown regions. Further, after the initial estimation of the alpha matte, a fully connected conditional random field (CRF) is used to correct the predicted matte at the pixel level. Our experiments show that visually plausible alpha mattes can indeed be produced.
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
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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.
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
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交大名義發表
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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.
AUTOMATIC GENERATION OF EPIPOLAR CURVES
(2018-01-01)
Umam, Ardian
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Hsieh, Yi-Yu
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Marsya, Jiwa Malem
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Chuang, Jen-Hui
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資訊工程學系
Fisheye camera is widely used in various applications because of its wider field-of-view. However, high distortion of images captured by fisheye cameras make it difficult for certain tasks which are based on traditional epipolar geometry (using epipolar lines) and stereo correspondence, such as depth map estimation. While most of existing depth map estimation methods use perspective-projection-based camera model, considering fisheye camera for depth map estimation will be beneficial because of its wider FOV. The availability of epipolar curves will be truly helpful for correspondence matching performed in depth map estimation. We propose a novel way of deriving epipolar curves using an auxiliary screen having benefits include: (i) deterministic results instead of statistical ones with accuracy within 1.5 pixels and (ii) the two epipoles as well as the epipolar curves in fisheye images can be obtained automatically, while unlimited number of the latter can be generated if needed.
BETTER FACE DETECTION WITH VANISHING POINT-BASED IMAGE RECTIFICATION
(2013)
Chang, Tien-Lung
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Wang, Ching-Ho
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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 novel face detection method based on the vanishing point of vertical lines in the scene to improve system performance in a common surveillance application. While most existing face datasets and detection techniques are based on the assumption that the camera has a similar height as the target faces, in practical situations the camera may be installed at different heights. Such discrepancy often degrades the detection performance of algorithms based on learning with certain (e. g., frontal) face orientation. In this paper we propose a transformation to rectify face images (video frames) such that it is not necessary to collect training data of different face orientations. Furthermore, with the proposed method there is no need to perform complex camera calibration. The only required information is the vanishing point of vertical lines, which can often be estimated easily. Experiments show prominent improvements in face detection performance can be obtained with the proposed image transformation.
Camera Self-Calibration from Tracking of Moving Persons
(2016-01-01)
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
In a video surveillance system with a single static camera, tracking results of moving persons can be effectively used for camera self-calibration. However, the current methods need to depend on robustness of both tracking and segmentation procedures. RANSAC has been widely used to remove outliers in finding the vertical vanishing point and the horizon line, but the performance is degraded when the proportion of outliers is high. Last but not least, all of them require excessive simplifications in the algorithmic procedures resulting in increasing reprojection error. In this paper, a robust segmentation and tracking system is applied to provide accurate estimation of head and foot locations of moving persons. The noise in the computation of vanishing points is handled by mean shift clustering and Laplace linear regression through convex optimization. We also propose to use the estimation of distribution algorithm (EDA) to search for the local optimal solution for camera calibration that minimizes average reprojection error on the ground plane, while relaxing the assumptions on camera parameters. Promising evaluations of the performance of our proposed method on real scenes are presented.
DEEP LEARNING-BASED HUMAN ACTIVITY ANALYSIS FOR AERIAL IMAGES
(2017-01-01)
Wang, Han-Yang
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Chang, Ya-Ching
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Hsieh, Yi-Yu
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Chen, Hua-Tsung
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Chuang, Jen-Hui
Due to the advantages of high mobility and the ability to fly in the sky, drone has inspired more and more applications in recent years. On the other hand, deep learning-based human activity analysis is an important research topic in security surveillance; however, there are few research works on such analysis with aerial images so far. Because of perspective projection, people in aerial images look tilted, which would degrade the performance of human activity analysis. In order to cope with the issue of perspective projection for aerial images, we modify the CNN architecture of a state-ofthe-art object detection method, YOLOv2 [12], and build an aerial image dataset with a drone for new model training. Finally, a post -processing method is proposed to classify the pose of a detected person as normal or abnormal, so that the task of human activity analysis with aerial images can be accomplished.
DEEP LEARNING-BASED OBSTACLE DETECTION AND DEPTH ESTIMATION
(2019-01-01)
Hsieh, Yi-Yu
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Lin, Wei-Yu
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Li, Dong-Lin
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Chuang, Jen-Hui
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資訊工程學系
This paper proposed a modified YOLOv3 which has an extra object depth prediction module for obstacle detection and avoidance. We use a pre-processed KITTI dataset to train the proposed, unified model for (i) object detection and (ii) depth prediction and use the AirSim flight simulator to generate synthetic aerial images to verify that our model can be applied in different data domains. Experimental results show that the proposed model compares favorably with other depth map prediction methods in terms of accuracy in the prediction of object depth for pre-processed KITTI dataset, while the unified approach can actually improve both (i) and (ii) at the same time.