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29 January 2007 Moving object detection under complex background using radial basis function neural network
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Proceedings Volume 6279, 27th International Congress on High-Speed Photography and Photonics; 62794U (2007) https://doi.org/10.1117/12.725457
Event: 27th International congress on High-Speed Photography and Photonics, 2006, Xi'an, China
Abstract
It is well known that moving object detection under complex background becomes more difficult because of moving cameras. According to the fact that background and objects have different motion, the moving scene can be decomposed into different regions with respect to their motion by means of a radial basis function(RBF) learning scheme. After global background motion compensation, five-dimensional (5-D) feature vectors include pixel intensities, current pixel coordinates and pixel dense optical flow field extracted from image sequences are treated as the inputs of the RBF network. The learning algorithm for the network minimizes a cost function derived from the Bayesian estimation theory. Each output unit of the network is associated to a moving object. Experimental results indicate the algorithm's validity after many complex sequences are tested.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zuomei Lai, Jingru Wang, and Qiheng Zhang "Moving object detection under complex background using radial basis function neural network", Proc. SPIE 6279, 27th International Congress on High-Speed Photography and Photonics, 62794U (29 January 2007); doi: 10.1117/12.725457; https://doi.org/10.1117/12.725457
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