This paper describes a new model of camera networks that can be used for environment monitoring and understanding. Such networks can be composed of both smart cameras, which benefit from high resolutions, powerful processing capabilities and strategic viewpoints on the environment, or with what we may call silly cameras, defined by much lower specifications. In this paper, we will demonstrate how our approach can reach efficient high-level understanding in spite of the limited information provided by each silly camera. We thus introduce the Ant-Cam model, inspired from the world of ants, which are able to solve complex problems by communicating despite their limited capabilities. The main idea is that data exchanged between the cameras is as important as the information extracted locally. Fully exploiting the cameras interactions without prior knowledge about the network configuration (e.g. camera location), the system is able to learn regularities and then infer from distributed sequences of events, passed between Ant-Cams. Once the system reaches a steady state, useful information can be inferred, such as the most used path or if space covering is sufficient.