Carolina Reta, Leopoldo Altamirano, Jesus A. Gonzalez, and Rafael Medina-Carnicer
People tracking, occlusion, human interaction, spatio-temporal features
In this work we investigate the problem of tracking multiple interacting people under uncontrolled stationary environments for intelligent surveillance applications. This domain is very challenging since the clothing appearance changes of the people over time make difficult the temporal association of their identities. The problem is emphasized when individuals move close to each other, are occluded, or abruptly change their trajectories. We propose a tracking graph that models spatial and temporal relationships among people in order to predict and resolve partial and total occlusions. When a total occlusion event occurs, the model generates three possible hypotheses about the location of the occluded person according to a human interaction analysis. This model is able to detect false positives and false negatives in the detection measurements and it can also estimate the location of missing or occluded people. Our approach was evaluated on benchmark sequences and results show how it outperforms state-of-the-art algorithms even in the presence of long periods of occlusion.
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