This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiplepeople, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graphthat models spatio-temporal relationships among attributes of interacting people to predict and resolve partialand total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about thelocation 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 duringocclusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimatethe location of missing or occluded people. Our algorithm performs acceptably under complex conditions, suchas partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusionsamong people, wrong or missing information on the detection of persons, as well as variation of the person’sappearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated ontest sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results showthat our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms.
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