Particle filtering and sparse sampling for multi-person 3D tracking

This paper presents a new 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. Two Monte Carlo based schemes adapted to the incoming 3D discrete data are introduced. First, a particle filtering technique is proposed relying on a volume likelihood function taking into account both occupancy and color information. Sparse sampling is presented as an alternative based on a sampling of the surface voxels in order to estimate the centroid of the tracked people. In this case, the likelihood function is based on local neighborhoods computations thus decreasing the computational load of the algorithm. A discrete 3D re-sampling procedure is introduced to drive these samples along time. Multiple targets are tracked by means of multiple filters and interaction among them is modeled through a 3D blocking scheme. Tests over annotated databases yield quantitative results showing the effectiveness of the proposed algorithms in indoor scenarios.