Artificial vision system applications have generated significant interest as they allow information to be obtained through one or several of the cameras that can be found in daily life in many places, such as parks, avenues, squares, houses, etc. When the aim is to obtain information from large areas, it can become complicated if it is necessary to track an object of interest, such as people or vehicles, due to the vision space that a single camera can cover; this opens the way to distributed zone monitoring systems made up of a set of cameras that aim to cover a larger area. Distributed zone monitoring systems add great versatility, becoming more complex in terms of the complexity of information analysis, communication, interoperability, and heterogeneity in the interpretation of information. In the literature, the development of distributed schemes has focused on representing data communication and sharing challenges. Currently, there are no specific criteria for information exchange and analysis in a distributed system; hence, different models and architectures have been proposed. In this work, the authors present a framework to provide homogeneity in a distributed monitoring system. The information is obtained from different cameras, where a global reference system is defined for generated trajectories, which are mapped independently of the model used to obtain the dynamics of the movement of people within the vision area of a distributed system, thus allowing for its use in works where there is a large amount of information from heterogeneous sources. Furthermore, we propose a novel similarity metric that allows for information queries from heterogeneous sources. Finally, to evaluate the proposed performance, the authors developed several distributed query applications in an augmented reality system based on realistic environments and historical data retrieval using a client–server model.