The VLSI Architecture Design of Moving Objects Detection with Adaptive Filter

• Main Authors: Tsung-Cheng Wu, 吳宗政
• Other Authors: Jen-Shiun Chiang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/74724642898673592732

碩士 === 淡江大學 === 電機工程學系碩士班 === 98 === Intelligent surveillance system is an important issue for the security consideration in recent years. The first step of the intelligent surveillance system is moving objects detection. A successful detection can reduce redundant data and provide helpful information for post-processing such as moving objects tracking and analysis. Therefore, the moving objects detection of an intelligent surveillance system is a basic but essential task. This thesis presents a new approach of moving objects detection by using the technique of least mean square (LMS) algorithm. This method can adapt the coefficients of low pass filter to the different environments. The low pass filter is used to reduce image resolution and noise effects such as Gaussian noise or fake motions. In the future, the high resolution images for surveillance system will be more common, and the computing time of the system will be longer. The system of high resolution images operated by the software platform may not achieve real-time, so we need to design and implement hardware architecture of the system to reduce the computing time for real-time processing. The proposed approach is further implemented by the VLSI architecture. For the implementation we follow the IC design flow of cell-based IC design. When image size is QVGA (320×240)，the system can reach 1291 FPS (Frame per second).In the same way to design the VLSI architecture, the system can reach 48 FPS as image size is Full HD (1920×1080). The proposed approach is compared with other methods, such as the technique of down-sampling, average filter, and SMDWT. The experimental results show that the accuracy rates of our approach are better than others in different environments. The accuracy rate for moving objects detection of the proposed approach is 84.72%.