Two key problems for camera networks that observe wide areas with many distributed cameras are self-localization and camera identification. Although there are many methods for localizing the cameras, one of the easiest and most desired methods is to estimate camera positions by having the cameras observe each other; hence the term self-localization. If the cameras have a wide viewing field, e.g. an omnidirectional camera, and can observe each other, baseline distances between pairs of cameras and relative locations can be determined. However, if the projection of a camera is relatively small on the image of other cameras and is not readily visible, the baselines cannot be detected. In this paper, a method is proposed to determine the baselines and relative locations of these “invisible” cameras. The method consists of two processes executed simultaneously: (a) to statistically detect the baselines among the cameras, and (b) to localize the cameras by using information from (a) and propagating triangle constraints. Process (b) works for the localization in the case where the cameras are observed each other, and it does not require complete observation among the cameras. However, if many cameras cannot be observed each other because of the poor image resolution, it dose not work. The baseline detection by process (a) solves the problem. This methodology is described in detail and results are provided for several scenarios.