Announcement for Downloading full text filePlease respect the Copyright Act.
All digital full text dissertation and theses from this website are authorized the copyright owners. These copyrighted full-text dissertation and theses can be only used for academic, research and non-commercial purposes. Users of this website can search, read, and print for personal usage. In respect of the Copyright Act of the Republic of China, please do not reproduce, distribute, change, or edit the content of these dissertations and theses without any permission. Please do not create any work based upon a pre-existing work by reproduction, Adaptation, Distribution or other means.
URN etd-0822105-170351 Statistics This thesis had been viewed 2496 times. Download 886 times. Author Chen-Rong Chang Author's Email Address firstname.lastname@example.org Department Computer Science and Enginerring Year 2005 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 58 Title The Application of Intelligent System to the Moving Object Recognition Keyword Intelligent System Intelligent System Abstract The monitoring system has been widely accepted recently. Most monitoring systems utilize the digital image storage device to store the captured pictures. The recent application focuses on recording those moving objects only. In the thesis, we propose an automatic surveillance system which employs the fuzzy inference, Back Propagation Network (BPN) and grey prediction theory. First, the system will read the image captured from the charge coupled device (CCD), and then we utilize the object detection technology to analyze the moving object. After deriving the moving object information, the grey prediction model is employed to process the human recognition, human’s height calculation, human’s moving path prediction and motorcyclist’s height calculation.
Due to the human’s skeleton is different from that of the animal, we can utilize the fuzzy inference to differentiate the moving objects belonging to human or animals. Therefore, we divide the skeleton into two categories: the human and the animal. Four skeleton models are built for human and animals, respectively. These models are sufficient to calculate the similarity of moving objects within an image. After recognizing a human, the system utilizes the well-known artificial neural network, back propagation network, to calculate the real height of that object. This information can help identify the moving objects. Followed by coupling with the GM(1,1) model, our system can predict the object future path. Based on the object moving path, the system can make an early-warning in case someone breaks into restricted area. Not only the theoretical study is studied, but also detailed simulation results are presented to verify the effectiveness of the proposed system.
Advisor Committee Yo-Ping Huang - advisor
none - co-chair
none - co-chair
Files Date of Defense 2005-07-06 Date of Submission 2005-08-22