发明名称 |
METHOD AND APPARATUS FOR MOVING OBJECT DETECTION USING PRINCIPAL COMPONENT ANALYSIS BASED RADIAL BASIS FUNCTION NETWORK |
摘要 |
A method for moving object detection based on a Principal Component Analysis-based Radial Basis Function network (PCA-based RBF network) includes the following steps. A sequence of incoming frames of a fixed location delivered over a network are received. A plurality of Eigen-patterns are generated from the sequence of incoming frames based on a Principal Component Analysis (PCA) model. A background model is constructed from the sequence of incoming frames based on a Radial Basis Function (RBF) network model. A current incoming frame is received and divided into a plurality of current incoming blocks. Each of the current incoming blocks is classified as either a background block or a moving object block according to the Eigen-patterns. Whether a current incoming pixel of the moving object blocks among the current incoming blocks is a moving object pixel or a background pixel is determined according to the background model. |
申请公布号 |
US2015279052(A1) |
申请公布日期 |
2015.10.01 |
申请号 |
US201414231637 |
申请日期 |
2014.03.31 |
申请人 |
National Taipei University of Technology |
发明人 |
Chen Bo-Hao;Huang Shih-Chia |
分类号 |
G06T7/20 |
主分类号 |
G06T7/20 |
代理机构 |
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代理人 |
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主权项 |
1. A moving object detection method based on a Principal Component Analysis-based Radial Basis Function network (PCA-based RBF network) comprising:
receiving a sequence of incoming frames of a fixed location delivered over a network; generating a plurality of Eigen-patterns from the sequence of incoming frames based on a Principal Component Analysis (PCA) model, wherein the PCA model comprises an optimal projection vector; constructing a background model from the sequence of incoming frames based on a Radial Basis Function (RBF) network model, wherein the RBF network model comprises an input layer having a plurality of input layer neurons, a hidden layer having a plurality of hidden layer neurons, and an output layer having an output layer neuron, and wherein there exists a weight between each of the hidden layer neurons and the output layer neuron; receiving a current incoming frame delivered over the network and partitioning the current incoming frame into a plurality of current incoming blocks; classifying each of the current incoming blocks as either a background block or a moving object block according to the Eigen-patterns; and determining whether a current incoming pixel of the moving object blocks among the current incoming blocks is a moving object pixel or a background pixel according to the background model. |
地址 |
Taipei TW |