Browsing by Author "Hwang, Jenq-Neng"

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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 hierarchical push-pull scheme for peer-to-peer live streaming
(2008)
Gau, Victor
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Wang, Yi-Hsien
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Hwang, Jenq-Neng
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資訊工程學系
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Department of Computer Science
In this paper, we propose a hierarchical push-pull scheme for peer-to-peer (P2P) live streaming. In our scheme, a control topology is used for membership management, and a multi-source multicast tree for data delivery. The control topology facilitates peers to locate multiple sources for forwarding media content, and the multi-source multicast tree makes the system adaptive to node churn and packet loss. Simulation results show that our proposed method is significantly better than BT-Like systems.
A max-min fairness congestion control for layered streaming of scalable video
(2006-09-01)
Hsiao, Hsu-Feng
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Hwang, Jenq-Neng
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資訊工程學系
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Department of Computer Science
In a best-effort networking environment, efficient and fair congestion control is highly desired for every traffic flow to share the bandwidth appropriately. This paper proposes a congestion control algorithm for user datagram protocol rate-based layered streaming of scalable video, e.g., 3-D wavelet based scalable video streaming, which provides a variety of video bit rates. This proposed congestion control mechanism, as an extension of explicit control protocol that is a newly proposed congestion control. protocol believed to be superior to transport control protocol, accommodates both window-based and rate-based flows to the heterogeneous network environment which can include wired and wireless channels. This paper further introduces the notion of reserved packet length so that the traffic of layered video can better share the bandwidth of a network by taking account of the max-min fairness with other traffic.
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.
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.
Generalization Performance Analysis of Flow-based Peer-to-Peer Traffic Identification
(2008)
Wang, Yi-Hsien
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Gau, Victor
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Bosaw, Trevor
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Hwang, Jenq-Neng
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Lippman, Alan
In this paper, we develop a peer-to-peer (P2P) traffic identifier to facilitate quality of service (QoS) control in edge routers. Currently, since P2P applications consume a great percentage of Internet bandwidth, certain network optimization strategies are needed to improve the nerwork performance. Traffic identification is the most important component that could be adopted in these optimization strategies. In this paper, we focus on developing a machine learning strategy to perform quick identification, and continuous tracking of flows associated with various P2P media streaming and file sharing applications. With the use (if Random Forests (RF) and evaluated by using 10-fold cross validation, our method achieves greater than 98% accuracy rate and 89% precision rate of identifying the P2P flows, with less than 1% false positive rate. With the help of winner-lake-all strategy, the generalization performance of using the RF built with data collected from one network to classify flows in other networks can achieve accuracy of being over 97%, with the precision being over 81% and the FP rate being below 2%.
MULTIPLE-KERNEL ADAPTIVE SEGMENTATION AND TRACKING (MAST) FOR ROBUST OBJECT TRACKING
(2016)
Tang, Zheng
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Hwang, Jenq-Neng
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Lin, Yen-Shuo
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Chuang, Jen-Hui
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資訊工程學系
In a video surveillance system with static cameras, object segmentation often fails when part of the object has similar color with the background, resulting in poor performance of the subsequent object tracking. Multiple kernels have been utilized in object tracking to deal with occlusion, but the performance still highly depends on segmentation. This paper presents an innovative system, named Multiple-kernel Adaptive Segmentation and Tracking (MAST), which dynamically controls the decision thresholds of background subtraction and shadow removal around the adaptive kernel regions based on the preliminary tracking results. Then the objects are tracked for the second time according to the adaptively segmented foreground. Evaluations of both segmentation and tracking on benchmark datasets and our own recorded video sequences demonstrate that the proposed method can successfully track objects in similar-color background and/or shadow areas with favorable segmentation performance.
OLM: Opportunistic Layered Multicasting for Scalable IPTV over Mobile WiMAX
(2012-03-01)
Huang, Chih-Wei
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Huang, Shiang-Ming
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Wu, Po-Han
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Lin, Shiang-Jiun
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Hwang, Jenq-Neng
We propose Opportunistic Layered Multicasting (OLM), a joint user scheduling and resource allocation algorithm that provides enhanced quality and efficiency for layered video multicast over Mobile WiMAX. This work is a lead off and complete synergy of layered video multicasting with opportunistic concept. The target application is characterized by groups of users acquiring popular video programs over a fading channel. To accommodate various bandwidth requirements and device capability, video streams are coded into base and enhancement layers using scalable video coding technology. Correspondingly, the optimization problems, which select the best subset of users to receive a specific video layer and assign the most appropriate modulation and coding scheme for this video layer, are specifically formulated for both video layer types. We also design fast and effective algorithms to bridge the gap between theoretical throughput capacity and implementation concerns. Thus, the basic video quality can be efficiently guaranteed to all subscribers while creating most utility out of limited resources on enhancement information. To overcome the inevitable packet loss in a multicast session, an FEC rate adaptation scheme to approach theoretical performance is also presented. Favorable performance of the proposed algorithms is demonstrated by simulations utilizing realistic Mobile WiMAX parameters.
Reducing Feedback Load of Opportunistic Multicast Scheduling over Wireless Systems
(2010-12-01)
Huang, Shiang-Ming
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Hwang, Jenq-Neng
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Chen, Yaw-Chung
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資訊工程學系
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Department of Computer Science
Opportunistic multicast scheduling (OMS) requires the subscribers to feed back their instantaneous channel conditions frequently, and the feedback load increases linearly as the number of subscribers in the system increases. In this letter, we propose a Weakly Consistent OMS scheme for feedback reduction. The contribution of the proposed scheme is twofold: 1) feedback load is reduced while the multicast throughput performance remains comparable with the case of full feedback, and 2) no precise knowledge of the wireless fading channel is required. Performance evaluation through realistic WiMAX simulations shows that our proposed scheme can significantly reduce 65% of the feedback load while achieving 98% multicast throughput performance compared with the case of full feedback.
Reducing Feedback Load of Opportunistic Multicast Scheduling over Wireless Systems
(2010-12-01)
Huang, Shiang-Ming
;
Hwang, Jenq-Neng
;
Chen, Yaw-Chung
;
資訊工程學系
;
Department of Computer Science
Opportunistic multicast scheduling (OMS) requires the subscribers to feed back their instantaneous channel conditions frequently, and the feedback load increases linearly as the number of subscribers in the system increases. In this letter, we propose a Weakly Consistent OMS scheme for feedback reduction. The contribution of the proposed scheme is twofold: 1) feedback load is reduced while the multicast throughput performance remains comparable with the case of full feedback, and 2) no precise knowledge of the wireless fading channel is required. Performance evaluation through realistic WiMAX simulations shows that our proposed scheme can significantly reduce 65% of the feedback load while achieving 98% multicast throughput performance compared with the case of full feedback.