The current paper presents a low-complexity approach
to the problem of simultaneous tracking of several people
in low resolution sequences from multiple calibrated cameras.
Redundancy among cameras is exploited to generate
a discrete 3D colored representation of the scene. The proposed
filtering technique estimates the centroid of a target
using only a sparse set of points placed on its surface and
making this set evolve along time based on the seminal particle
filtering principle. In this case, the likelihood function
is based on local neighborhoods computations thus drastically
decreasing the computational load of the algorithm.
In order to handle multiple interacting targets, a separate
filter is assigned to each subject in the scenario while a
blocking scheme is employed to model their interactions.
Tests over a standard annotated dataset yield quantitative
results showing the effectiveness of the proposed technique
in both accuracy and real-time performance.
CitationCanton-Ferrer, C.; Casas, J.; Pardas, M. Real-time 3D multi-person tracking using Monte Carlo surface sampling. A: IEEE Computer-Society Conference on Computer Vision and Pattern Recognition Workshops. "2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops". San Francisco: 2010, p. 40-46.
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