Source: http://etds.lib.tku.edu.tw/etdservice/view_metadata?etdun=U0002-1507201013094600
Timestamp: 2019-04-25 19:54:14+00:00

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英文摘要 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.
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