@inproceedings{boulmerka_background_2015,

title = {Background modeling in videos revisited using finite mixtures of generalized Gaussians and spatial information},

author = {A. Boulmerka and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84956615464&doi=10.1109%2fICIP.2015.7351487&partnerID=40&md5=e6700460a4cd3a6db31f012307db11f4},

doi = {10.1109/ICIP.2015.7351487},

isbn = {15224880 (ISSN); 978-147998339-1 (ISBN)},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings - International Conference on Image Processing, ICIP},

volume = {2015-December},

pages = {3660–3664},

publisher = {IEEE Computer Society},

abstract = {This paper presents a new statistical approach combining temporal and spatial information for robust background subtraction (BS) in videos. Temporal information couples finite mixtures of generalized Gaussians (MoGG) and temporal cooccurrence analysis of forground/background data. Spatial information combines multi-scale correlation analysis and histogram matching. Our approach fuses both information to perform efficient BS in the presence of shadows, illumination changes and various complex background dynamics. Comparison with recent state-of-the-art methods on standard datasets has demonstrated the performance of our method in terms of precision and computational efficiency. © 2015 IEEE.},

note = {Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP},

keywords = {Background subtraction, Mixture models, spatial information modeling, temporal co-occurrence},

pubstate = {published},

tppubtype = {inproceedings}

}