Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods

—This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolving models are proposed for automatic graph structure initialisation, incorporation of new nodes, unexisting nodes removal and the edge update. Both the state and the graph structure are updated based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over scenarios for group motion estimation in urban environments. Results with challenging scenarios with merging, splitting and crossing of groups are prese...

• Type: Journal
• Year: 2011
• Where: TSP
• Authors: Amadou Gning, Lyudmila Mihaylova, Simon Maskell, Sze Kim Pang, Simon J. Godsill
• Keywords: Graph Structure, Group, Motion Estimation, TSP 2011