Virtual agents and video surveillance
10637
Virtual Agent paper
published in 2010
by Carles Fernández, Pau Baiget, Xavier Roca and Jordi Gonzàlez
in
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The fields of segmentation, tracking and behavior analysis demand for challenging video resources to test, in a scalable manner, complex scenarios like crowded environments or scenes with high semantics. Nevertheless,
existing public databases cannot scale the presence of appearing agents, whichwould be useful to study long-term occlusions and crowds. Moreover, creating these resources is expensive and often too particularized
to specific needs. We propose an augmented reality framework to increase the complexity of image sequences in terms of occlusions and crowds, in a scalable and controllable manner. Existing datasets can be increased with augmented sequences containing
virtual agents. Such sequences are automatically annotated, thus facilitating evaluation in terms of segmentation, tracking, and behavior recognition. In order to easily specify the desired contents, we propose a natural language interface to convert input sentences into virtual agent behaviors. Experimental tests and validation in indoor, street, and soccer environments are provided to showthe feasibility of the proposed approach in terms of robustness, scalability, and semantics.