@inproceedings{allili_object_2007-1,

title = {Object contour tracking in videos by using adaptive mixture models and shape priors},

author = {M. S. Allili and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-60949085472&partnerID=40&md5=ba63e1abbabcfdd48583b41f700508ef},

isbn = {978-0-415-43349-5},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings of the International Symposium CompIMAGE 2006 - Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications},

pages = {47–52},

address = {Coimbra},

abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The method is based on object mixture matching between successive frames of the sequence by using active contours. Only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contour on a current frame aims to find the maximum fidelity of the mixture likelihood for the same object between successive frames while having the best fit of the mixture parameters to the homogenous parts of the objects. To permit for a precise and robust tracking, region, boundary and shape information are coupled in the model. The method permits for tracking multi-class objects on cluttered and non-static backgrounds. We validate our approach on examples of tracking performed on real video sequences. © 2007 Taylor & Francis Group.},

keywords = {Active contours, Best fits, Current frames, Image matching, Maximum likelihood, Mixture models, Mixtures, Multi class, Non-static backgrounds, Object contours, Object tracking algorithms, Real video sequences, Robust tracking, Shape informations, Shape priors, Video recording, Video sequences},

pubstate = {published},

tppubtype = {inproceedings}

}