Neighbour consensus for distributed visual tracking

We propose N-consensus, an algorithm that reduces the cost of the consensus process for distributed visual target tracking without compromising on tracking accuracy. N-consensus fuses target posteriors computed by viewing nodes (i.e. the cameras viewing the same target) only and limits the number of nodes participating in consensus to those within a specified number of hops from the viewing nodes. The number of hops is computed based on viewing and communication ranges to identify all nodes within twice the viewing range from the viewing nodes. Unlike average consensus, the proposed N-consensus does not require prior knowledge of node connectivity because we employ an improved fast covariance intersection algorithm during consensus update.