000145940 001__ 145940
000145940 005__ 20190812205355.0
000145940 02470 $$2ISI$$a000260659800009
000145940 037__ $$aCONF
000145940 245__ $$aMulti-Camera Tracking and Atypical Motion Detection with Behavioral Maps
000145940 269__ $$a2008
000145940 260__ $$c2008
000145940 336__ $$aConference Papers
000145940 520__ $$aWe introduce a novel behavioral model to describe pedestrians motions, which is able to capture sophisticated motion patterns resulting from the mixture of different categories of random trajectories. Due to its simplicity, this model can be learned from video sequences in a totally unsupervised manner through an Expectation-Maximization procedure. When integrated into a complete multi-camera tracking system, it improves the tracking performance in ambiguous situations, compared to a standard ad-hoc isotropic Markovian motion model. Moreover, it can be used to compute a score which characterizes atypical individual motions. Experiments on outdoor video sequences demonstrate both the improvement of tracking performance when compared to a state-of-the-art tracking system and the reliability of the atypical motion detection.
000145940 6531_ $$amulticamera
000145940 6531_ $$abehavior analysis
000145940 6531_ $$abehavior analysis
000145940 700__ $$aBerclaz, Jérôme
000145940 700__ $$0240254$$g146262$$aFleuret, François
000145940 700__ $$aFua, Pascal$$g112366$$0240252
000145940 7112_ $$dOctober 12-18, 2008$$cMarseille$$aProceedings of the 10th European Conference on Computer Vision
000145940 8564_ $$zn/a$$yPreprint$$uhttps://infoscience.epfl.ch/record/145940/files/BerclazFF08c.pdf$$s1680756
000145940 909C0 $$xU10659$$pCVLAB$$0252087
000145940 909C0 $$pLIDIAP$$0252189$$xU10381
000145940 909CO $$qGLOBAL_SET$$pconf$$pSTI$$pIC$$ooai:infoscience.tind.io:145940
000145940 917Z8 $$x118664
000145940 917Z8 $$x118664
000145940 937__ $$aLIDIAP-CONF-2008-006
000145940 970__ $$aBerclaz_ECCV_2008/LIDIAP
000145940 973__ $$rNON-REVIEWED$$sPUBLISHED$$aEPFL
000145940 980__ $$aCONF