The paper presents a layered architecture that improves software modularity and reduces computational and communication overhead for systems requiring data from sensors in order to perform domain-related elaborations (e.g., tracking and surveillance systems). Each layer manages hypotheses that are abductions related to objects modeling the ”real world” at a specific abstraction level, from raw data up to domain concepts. Each layer, by analyzing hypotheses coming from the lower layer, abduces new hypotheses regarding objects at a higher level of abstraction (e.g., from image blobs to identified people) and formulates timed previsions about objects. The failure of a prevision causes a hypothesis to flow up-stream. In turn, previsions can flow downstream, so that their verification is delegated to the lower layers. The proposed architectural patterns have been reified in a Java framework, which is being exploited in an experimental multi-camera tracking system.