Paper Info
Title
Adaptive Gmm And Bp Neural Network Hybrid Method For Moving Objects Detection In Complex Scenes
Abstract
Moving foreground objects detection in complex scenes is a tough job because it requires high recognition accuracy. Adaptive Gaussian mixture model (AGMM) can be used to extract the foreground objects and it shows good performance, however, the detection quality of the foreground objects under complex scenes is not excellent. In this paper, an AGMM and BP neural network hybrid method is proposed, which is used to extract the foreground objects in complex scenes such as, dynamic backgrounds, illumination changes and moving shadows. In this method, an improved BP neural network is used to post-process the images of the foreground objects that are extracted from the AGMM. The neural network has strong robustness by learning the statistical features of the images. Momentum term and adaptive learning rate are added in the BP neural network algorithm to improve the training speed and robustness of the network. The experimental results show that the proposed AGMM and BP neural network hybrid method can extract the complete foreground objects effectively when compared with some other moving objects detection algorithms.
Year
DOI
Venue
2019
10.1142/S0218001419500046
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Moving foreground object detection, adaptive Gaussian mixture model, statistical feature, neural network
Pattern recognition,Artificial intelligence,Artificial neural network,Mixture model,Mathematics
Journal
Volume
Issue
ISSN
33
2
0218-0014
Citations 
PageRank 
References 
0
0.34
6
Authors
9
Name
Order
Citations
PageRank
Xianfeng Ou102.03
Pengcheng Yan200.68
Wei He362.86
Yong Kwan Kim411.37
Guoyun Zhang5187.38
Xin Peng673.17
Wenjing Hu7116.39
Jianhui Wu8104.26
Longyuan Guo9104.59