Reta, CarolinaAltamirano, JesúsGónzalez, Jesús A.Medina-Carnicer, R.2017-12-05T12:43:03Z2017-12-05T12:43:03Z2015http://hdl.handle.net/10396/15655This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiple
people, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graph
that models spatio-temporal relationships among attributes of interacting people to predict and resolve partial
and total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about the
location of the occluded person considering three cases: (a) the person keeps the same direction and speed,
(b) the person follows the direction and speed of the occluder, and (c) the person remains motionless during
occlusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimate
the location of missing or occluded people. Our algorithm performs acceptably under complex conditions, such
as partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusions
among people, wrong or missing information on the detection of persons, as well as variation of the person’s
appearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated on
test sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results show
that our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms.application/pdfengIS&T and SPIEhttps://creativecommons.org/licenses/by-nc-nd/4.0/Journal of Electronic Imaging 24(1), 013015 (2015)People trackingOcclusionTracking graphHypothesis managementSpatio-temporal featuresVideo surveillanceThree hypothesis algorithm with occlusion reasoning for multiple people trackinginfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1117/1.JEI.24.1.013015info:eu-repo/semantics/openAccess