Foreground Object Detection based on Multi-model Background Maintenance and Its DSP Implementation

• Main Authors: Wen-Tsai Sheu, 許文財
• Other Authors: 蔡宗漢
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/74764026691360295280

碩士 === 國立中央大學 === 電機工程研究所 === 95 === Foreground object detection in a scene, often referred to as “background subtraction”, is a critical early in step in most computer vision applications in domains such as video surveillance, traffic monitoring, human motion capture and human-computer interaction. Background subtraction is a widely used approach for detecting moving objects from the difference between the current frame and a reference frame, often called the “background image”, or “background model”. As a basic, the background image must be a representation of the scene with no moving objects and must be kept regularly updated so as to adapt to the varying luminance conditions and some problems described in the introduction. For this reason, how to maintain a background image is very important issue. In the thesis, in order to acquire accurate foreground object detection with above some problems, a Multi-model Background Maintenance (MBM) algorithm is proposed. A MBM framework contains two principal features to construct a practice background image with time-varying background changes. Under this framework, the background image is represented by the most significant and recurrent features, the principal features at each pixel. Principal features consist of static and dynamic features to represent background pixels. A MBM includes two major procedures, background maintenance and foreground extraction. Experiments show proposed method provides good results on different kinds of sequences. Quantitative evaluation and comparison with the existing method show that the proposed method provides much improved results with lower complexity. Finally, we use IEKC64x platform to implement MBM algorithm for obtaining real time foreground object detection.