Abstract
In this study, one adaptive spatiotemporal background modelling algorithm is proposed for robust and reliable moving object detection in dynamic scene. First, a modified adaptive Gaussian mixture model (GMM) is presented to describe the temporal distribution of each pixel, based on which the spatial distribution of background is constructed by using non-parametric density estimation. By fusing the temporal and spatial distribution model, a heuristic strategy is presented for background subtraction. To reduce the computational cost, a novel criterion for adaptively determining the components number of GMM and the integral image method for calculating the spatial distribution model are proposed. Several experiments show that the proposed method can effectively reduce false positives caused by sudden or gradual changes of the background, and maintains lower false negatives, compared with some representative algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
