Robust diffusion-based unsupervised object labelling in distributed camera networks

Recently, a new ICT paradigm emerged, which considers Multiple Devices that cooperate in Multiple Tasks (MDMT). Under this paradigm, cooperation among the nodes can be beneficial when subsets of the nodes share common interests or observations. For cooperation to be successful, it is thus necessary to account for a decentralized labelling scheme that allows to uniquely identify every object of interest. Such labelling not only ensures proper data exchange among the nodes but also allows the formation of interest-specific clusters and hence, might also be beneficial from a communications cost perspective. The research question addressed in this paper is to develop robust distributed labelling strategies in the context of camera networks where no central unit is available for fusing all the information. Simulation results demonstrate that a high labelling accuracy can be achieved in the considered setup (planar scene) with a correct classification performance close to the centralized solution. The proposed methodology is a promising strategy for distributed clustering in camera networks that can be extended to more complex scenarios.