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Three hypothesis algorithm with occlusion reasoning for multiple people tracking

[+] Author Affiliations
Carolina Reta

National Institute for Astrophysics, Optics, and Electronics, Department of Computer Science, Luis Enrique Erro No. 1, Puebla 72840, Mexico

Leopoldo Altamirano

National Institute for Astrophysics, Optics, and Electronics, Department of Computer Science, Luis Enrique Erro No. 1, Puebla 72840, Mexico

Jesus A. Gonzalez

National Institute for Astrophysics, Optics, and Electronics, Department of Computer Science, Luis Enrique Erro No. 1, Puebla 72840, Mexico

Rafael Medina-Carnicer

University of Cordoba, Department of Computing and Numerical Analysis, Campus de Rabanales, Edificio Einstein, 3a planta, Cordoba 14071, Spain

J. Electron. Imaging. 24(1), 013015 (Jan 13, 2015). doi:10.1117/1.JEI.24.1.013015
History: Received May 29, 2014; Accepted December 8, 2014
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Abstract.  This 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.

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Citation

Carolina Reta ; Leopoldo Altamirano ; Jesus A. Gonzalez and Rafael Medina-Carnicer
"Three hypothesis algorithm with occlusion reasoning for multiple people tracking", J. Electron. Imaging. 24(1), 013015 (Jan 13, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.1.013015


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