Browsing by Author "Huang, Hao-Yu"

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A Real-Time Vision System for Nighttime Vehicle Detection and Traffic Surveillance
(2011-05-01)
Chen, Yen-Lin
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Wu, Bing-Fei
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Huang, Hao-Yu
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Fan, Chung-Jui
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電控工程研究所
This paper presents an effective traffic surveillance system for detecting and tracking moving vehicles in nighttime traffic scenes. The proposed method identifies vehicles by detecting and locating vehicle headlights and taillights using image segmentation and pattern analysis techniques. First, a fast bright-object segmentation process based on automatic multilevel histogram thresholding is applied to effectively extract bright objects of interest. This automatic multilevel thresholding approach provides a robust and adaptable detection system that operates well under various nighttime illumination conditions. The extracted bright objects are then processed by a spatial clustering and tracking procedure that locates and analyzes the spatial and temporal features of vehicle light patterns, and identifies and classifies moving cars and motorbikes in traffic scenes. The proposed real-time vision system has also been implemented and evaluated on a TI DM642 DSP-based embedded platform. The system is set up on elevated platforms to perform traffic surveillance on real highways and urban roads. Experimental results demonstrate that the proposed traffic surveillance approach is feasible and effective for vehicle detection and identification in various nighttime environments.
A vision-based blind spot warning system for daytime and nighttime driver assistance
(2013-04-01)
Wu, Bing-Fei
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Huang, Hao-Yu
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Chen, Chao-Jung
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Chen, Ying-Han
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Chang, Chia-Wei
This paper proposes an effective blind spot warning system (BSWS) for daytime and nighttime conditions. The proposed BSWS includes camera models of a dynamic calibration and blind spot detection (BSD) algorithms for the daytime and nighttime. Under daytime conditions, the proposed system presents the Horizontal Edge and Shadow Composite Region (HESCR) method to extract the searching region and to acquire the shadow location of the targeted vehicles. Additionally, to detect vehicles at nighttime road scenes, the proposed system extracts bright objects and recognizes the paired headlights of the targeted vehicles for the BSD. The BSWS is implemented on a DSP-based embedded platform. The results of the BSWS are obtained by conducting practical experiments on our camera-assisted car on a highway in Taiwan under both nighttime and daytime conditions. Experimental results show that the proposed BSWS is feasible for vehicle detection and collision warning in various daytime and nighttime road environments. (C) 2013 Elsevier Ltd. All rights reserved.
Auto-Scaling Mechanism for Cloud Resource Management Based on Client-Side Turnaround Time
(2016)
Liu, Xiao-Long
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Yuan, Shyan-Ming
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Luo, Guo-Heng
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Huang, Hao-Yu
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資訊工程學系
Currently, providers of Software as a service (SaaS) can use Infrastructure as a Service (IaaS) to obtain the resources required for serving customers. SaaS providers can save substantially on costs by using resource-management techniques such as auto scaling. However, in most current auto-scaling methods, server-side system information is used for adjusting the amount of resources, which does not allow the overall service performance to be evaluated. In this paper, a novel auto-scaling mechanism is proposed for ensuring the stability of service performance from the client-side of view. In the proposed mechanism, turnaround time monitors are deployed as clients outside the service, and the information collected is used for driving a dynamic auto-scaling operation. A system is also designed to support the proposed auto scaling mechanism. The results of experiments show that using this mechanism, stable service quality can be ensured and, moreover, that a certain amount of quality variation can be handled in order to allow the stability of the service performance to be increased.
Cloud Resource Management With Turnaround Time Driven Auto-Scaling
(2017-01-01)
Liu, Xiaolong
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Yuan, Shyan-Ming
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Luo, Guo-Heng
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Huang, Hao-Yu
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Bellavista, Paolo
Cloud resource management research and techniques have received relevant attention in the last years. In particular, recently numerous studies have focused on determining the relationship between server side system information and performance experience for reducing resource wastage. However, the genuine experiences of clients cannot be readily understood only by using the collected server-side information. In this paper, a cloud resource management framework with two novel turnaround time driven auto-scaling mechanisms is proposed for ensuring the stability of service performance. In the first mechanism, turnaround time monitors are deployed in the client-side instead of the more traditional server-side, and the information collected outside the server is used for driving a dynamic auto-scaling operation. In the second mechanism, a schedule-based auto scaling preconfiguration maker is designed to test and identify the amount of resources required in the cloud. The reported experimental results demonstrate that using our original framework for cloud resource management, stable service quality can be ensured and, moreover, a certain amount of quality variation can be handled in order to allow the stability of the service performance to be increased.
Degraded License Plate Recognition System for Town Buses on Highway
(2013)
Wu, Bing-Fei
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Huang, Hao-Yu
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Chen, Yen-Lin
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Lin, Hsin-Yu
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Lee, Tsu-Tian
This study presented a Degraded License Plate Recognition (DLPR) to identify town buses on highway. Since the License Plates (LPs) of town buses are influenced by the external conditions and become hardly distinguished by the Optical Character Recognition (OCR) method. The proposed DLPR system adopts Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM)-for classification-based solution. The proposed system implements a two-level LPR, including LP localization and character recognition. For the recognition efficiency of the proposed system, two trained databases for LP localization and character recognition are reduced by the mathematical and feature-based approaches, respectively, and thus the searching time can be saved significantly. To certify the performance of recognizing the degraded LPs, the proposed system is demonstrated by physical degraded LP images, and thus the results reveal that the proposed system overcomes several degraded LP images with reasonable recognition accuracy.
MPEG-2/4 Low-Complexity Advanced Audio Coding Optimization and Implementation on DSP
(2010-05-01)
Wu, Bing-Fei
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Huang, Hao-Yu
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Chen, Yen-Lin
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Peng, Hsin-Yuan
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Huang, Jia-Hsiung
This study presents several optimization approaches for the MPEG-2/4 Audio Advanced Coding (AAC) Low Complexity (LC) encoding and decoding processes. Considering the power consumption and the peripherals required for consumer electronics, this study adopts the TI OMAP5912 platform for portable devices. An important optimization issue for implementing AAC codec on embedded and mobile devices is to reduce computational complexity and memory consumption. Due to power saving issues, most embedded and mobile systems can only provide very limited computational power and memory resources for the coding process. As a result, modifying and simplifying only one or two blocks is insufficient for optimizing the AAC encoder and enabling it to work well on embedded systems. It is therefore necessary to enhance the computational efficiency of other important modules in the encoding algorithm. This study focuses on optimizing the Temporal Noise Shaping (TNS), Mid/Side (M/S) Stereo, Modified Discrete Cosine Transform (MDCT) and Inverse Quantization (IQ) modules in the encoder and decoder. Furthermore, we also propose an efficient memory reduction approach that provides a satisfactory balance between the reduction of memory usage and the expansion of the encoded files. In the proposed design, both the AAC encoder and decoder are built with fixed-point arithmetic operations and implemented on a DSP processor combined with an ARM-core for peripheral controlling. Experimental results demonstrate that the proposed AAC codec is computationally effective, has low memory consumption, and is suitable for low-cost embedded and mobile applications.
The Blind Spot Warning System for Daytime and Nighttime
(2012)
Wu, Bing-Fei
;
Huang, Hao-Yu
;
Chen, Chao-Jung
;
Chen, Ying-Han
;
Chang, Chia-Wei
This paper proposes an effective blind spot detection and warning system in the daytime and nighttime condition. In the daytime condition, the proposed system presents the Horizontal Edge and Shadow Composite Region (HESCR) method to extract the searching region and to acquire the shadow location of the targeted vehicles. Additionally, to overcome the nighttime scene, the proposed system extracts the bright objects and recognizes the paired headlights of the targeted vehicles for the BSD. The results of the proposed system are yielded by the practical experiments on the highway in Taiwan at the nighttime and daytime conditions. Experimental results show that the proposed BSD system is feasible and effective for vehicle detection and identification in the daytime and nighttime environments.
The single-pass perceptual embedded zero-tree coding implementation on DSP
(2012-09-01)
Wu, Bing-Fei
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Huang, Hao-Yu
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Chen, Yen-Lin
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電控工程研究所
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Institute of Electrical and Control Engineering
This paper proposes a block-edge-based Single-Pass Perceptual Embedded Zero-tree Coding (SPPEZC) method. SPPEZC combines two novel compression concepts, called Block-Edge Detection (BED) and the Low-Complexity and Low-Memory Entropy Coder (LLEC), for coding efficiency and quality. Because the edge information can provide beneficial cues for preserving the perceptual quality of compressed images, this paper presents an effective combinative coding scheme, called Single-Pass Perceptual Embedded Zero-tree Coding (SPPEZC), which integrates the improved LLEC and the block-edge information. This approach provides improved perceptual quality in compressed images. Based on the block-edge information, this paper proposes an adaptive architecture for adjusting the quantization table and subsequently coding the quantized coefficients with the LLEC. The proposed SPPEZC approach was implemented and evaluated on both PC-based and DSP-based embedded platforms. Experimental results and comparisons demonstrate that the proposed SPPEZC technique provides computational efficiency as well as satisfactory perceptual quality in compressed images. (C) 2012 Elsevier Ltd. All rights reserved.