Camera networks are widely used for security and tracking. Knowledge of camera locations and geometric constraints in the environment are usually assumed in order to accomplish these tasks. However, many of these tasks do not require actual localization. Topological information about the network coverage is many times sufficient. In this work, a simplicial representation called the CN-Complex is presented which captures accurate topological information about the coverage of the network. The construction process of this representation relies on the detection of occlusion events. Occlusions are shown to occur when certain generalized topological invariants are violated. The use of these sparse events leads to algorithms which require the extraction of information from continuous observations. The CN-Complex is shown to be useful for navigation and path identification purposes. Augmenting this representation leads to the discovery of relations between camera pairs providing relative positions at different degrees of accuracy. These relations create a bridge between a purely topological model and a fully localized network. Several theoretical results are shown for occlusion detection and topology recovery, which are then validated by simulations and experiments.