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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/52499

    Title: A study on discrete wavelet transform for video compression and intelligent video surveillance system
    Other Titles: 以離散小波轉換應用於視訊壓縮與智慧型視訊監控系統之探討
    Authors: 夏至賢;Hsia, Chih-hsien
    Contributors: 淡江大學電機工程學系博士班
    江正雄;Chiang, Jen-shiun
    Keywords: 智慧型監控系統;提升式離散小波轉換;直接低低頻遮罩法;高解析度;intelligent video surveillance system;lifting-based discrete wavelet transform;direct LL-mask based scheme;high resolution
    Date: 2010
    Issue Date: 2010-09-23 17:51:41 (UTC+8)
    Abstract: 本論文將提供一個具有低成本且有效的二維離散小波轉換應用於智慧型視訊監控系統。其目的是將此技術應用於視訊壓縮以及電腦視覺中,利用離散小波轉換的特色發展出低轉置記憶體的超大型積體電路架構以及快速物件偵測演算法,以達到智慧型視訊監控系統的規格,並且兼具有簡單性、即時性、以及安全性的功能。在本論文中,我們將研究提升式離散小波轉換所產生出的缺點進而改良其演算法,再應用於智慧型視訊監控系統與視訊資料壓縮技術中並且達到即時處理。
    This thesis attempts to develop a low cost, practical application of a two-dimensional (2-D) discrete wavelet transform (DWT) to an intelligent video surveillance system. The goal is to combine the video compression and computer vision processing used in a wavelet-based system and to adopt DWT to develop a low transpose memory VLSI architecture and a fast object detection algorithm that can meet the specifications of intelligent video surveillance and can also process information easily, in real-time, and with safe functioning. This thesis investigates the fundamental concept behind the wavelet transform and provides an overview of some improved algorithms for lifting-based DWT (LDWT). The video surveillance system is able to detect moving object events, and video compression information is captured by surveillance cameras in real-time.
    Wavelet transformation has become increasingly important in image compression and processing since wavelets allow both simultaneous spatial and frequency analysis and scalable video processing. This thesis presents two new approaches. First, we propose new hardware architectures to address critical issues in 2-D dual-mode LDWT (supporting 5/3 lossless and 9/7 lossy coding modes). A considerably large transpose memory is the most critical requirement for LDWT. The proposed architecture can support high-resolution videos and reduce the internal memory requirement significantly. In our LDWT approach, the signal flow is revised from row-wise only to mixed row- and column-wise, and a new architecture, called interlaced read scan architecture (IRSA), is used to reduce the transpose memory. With the IRSA approach, the transpose memory size is only 2N or 4N (5/3 or 9/7 mode) for an N´N DWT. In addition, the proposed 2-D LDWT operates with parallel and pipelined schemes that increase its operation speed. It can be applied to real-time video operations for Motion-JPEG2000. Second, we propose the called symmetric mask-based DWT (SMDWT) for an intelligent video surveillance system. SMDWT improves the critical issue of the 2-D LDWT, and then obtains the benefit of low latency, reduced complexity, and low transpose memory. A highly precise and real-time moving object detection algorithm based on a low complexity SMDWT offers a mechanism for the sequential detection of both indoor (all day) and outdoor (all day) scenes. Computer simulations verified that the present algorithm performs well. It has high accuracy rate of more than 89.59% on average and the average frame rate can reach 47.1 frame per second (FPS).
    The abovementioned two algorithms for the LDWT were improved. The experimental results indicate that the proposed methods can provide low transpose memory and real-time processing for a high resolution intelligent video surveillance system.
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Thesis

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