Title :
Gaussian Mixture Model With Advanced Distance Measure Based on Support Weights and Histogram of Gradients for Background Suppression
Author :
Mukherjee, Dipankar ; Wu, Q. M. Jonathan ; Thanh Minh Nguyen
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Windsor, Windsor, ON, Canada
Abstract :
The proposed work is targeted toward improving the Gaussian mixture model (GMM) for the background suppression-based moving object detection. The GMM has been widely used for moving object detection due to its high applicability. However, the GMM cannot properly model noisy or nonstationary backgrounds and fails to discriminate between the foreground and background modes. The extensions to GMM provide increased accuracy in expense of complex implementation and reduced applicability. In response, this work proposes two simple improvements: 1) a novel distance measure based on local support weights and histogram of gradients to provide distinct cluster values; and 2) use of background layer concept to properly segment the foreground. The method also uses variable number of clusters for generalization. The main advantages of the method are implicit use of pixel relationships through distance measure with least modification to the conventional GMM and effective background noise removal through the use of background layer concept with no postprocessing involved. The extensive experimentations on various types of video sequences are performed to validate the improvement in accuracy compared to the GMM and a number of state-of-the-art methods.
Keywords :
Gaussian processes; image denoising; image motion analysis; image sequences; mixture models; object detection; video signal processing; GMM; Gaussian mixture model; background noise removal; background suppression; gradient distance measure; gradient support weights; histogram; moving object detection; video sequences; Algorithm design and analysis; Clustering algorithms; Complexity theory; Equations; Histograms; Image color analysis; Niobium; Background suppression; Gaussian mixture model (GMM); histogram of gradients (HoGs); moving object detection; support weight (SW);
Journal_Title :
Industrial Informatics, IEEE Transactions on
DOI :
10.1109/TII.2013.2294134