Data association for people tracking using multiple cameras

In this paper, we present a data association algorithm for people tracking in a 3D world using multiple cameras. Our approach expands an independent partitioned particle filter with a data association vector. For the association parameter, we propose a proposal function using likelihood functions based on color and distance. This proposed algorithm solves the data association problem without dramatically increasing the computational complexity even in the case of trajectories that cross.