Algorithm, Design, Theory

Camera networks are widely used for tasks such as surveil-lance, monitoring and tracking. In order to accomplish these tasks, knowledge of localization information such as camera locations and other geometric constraints about the environment (e.g. walls, rooms, and building lay-out) are typically considered to be essential. However, this information is not required for tasks such as es-timating the topology of camera network coverage, or coordinate-free object tracking and navigation. In this paper, we propose a simplicial representation (called CN-Complex) that can be constructed from discrete lo-cal observations, and utilize this novel representation to recover topological information of the network coverage. We prove that our representation captures the correct topological information for coverage in 2.5D layouts, and demonstrate its utility in simulations as well as an experimental setup. Our proposed approach is partic-ularly useful in the context of ad-hoc camera networks in indoor/outdoor urban environments with distributed but limited computational power and energy