## 2 Related Work
The INSTEAD architecture has been conceived to integrate autonomous sensor-based systems, where decision-making at sensor/component level is actually performed before message generation for the C&C. Additionally, in INSTEAD, performance issues have been carefully considered at design time due to strict real-time requirements. Therefore, data fusion and threat detection are performed in memory with the goal to increase the system performance. For a similar reason, the message payload of the components uses a simple JavaScript Object Notation (JSON)-based [19] structure, which is less verbose than that based on XML [20] defined by SAPIENT. A comparison of the methods used by the two-dimensional video (2D Video) and the three-dimensional video (3D Video) systems to support data fusion with respect to other works is also relevant. With respect to 2D Video person localization and re-identification capability, common deep learning technology [21] can reach high rank-1 accuracy on a large public dataset [22], but it includes facial information, and, in general, it does not generalize well to other environments, which makes it less suitable for practical applications. The 2D Video pipeline used in the INSTEAD architecture applies a new strategy for rapid Re-ID retraining [23] on anonymized person detections [24] to enhance privacy and increase flexibility for deployment in new environments. Tracking person movements using three-dimensional sensor approaches instead of two-dimensional cameras can offer improved tracking ability and activity estimation accuracy [25]. Accurate tracking of human movement is essential in multi-sensor systems, where the target is identified with one system, and the confirmation or final analysis is performed with another. High tracking accuracy ensures, especially in crowded spaces, that a hand-over between sensor systems is successful and that the effective analysis is performed on the correct target person. High accuracy requirement is even higher if person movement prediction is required for compensating delays caused by, for example, network communications latencies or adjustment delays in mechanical operations of pan-tilt-zoom (PTZ) sensors. More generally, Stone Soup is a very recent architectural framework devoted to systems for tracking and state estimation of moving objects. This is an open-source software project that produced a first release in 2019 and which is evolving thanks to a collaborative development, as described in the paper by [PERSON] et al. [26]. In particular, Stone Soup is primarily promoted as a generic and modular architectural framework for upload, test, and benchmark, which are different types of tracking solutions. To this aim, various implementations are provided in the framework. Person tracking in wide areas based on re-identification is not currently available in the framework, and the possibility to define workflows combining different video sensor data fusion activities as those implemented by INSTEAD is not straightforward. This workflow is implemented by the C&C in a declarative logic-based language for data-stream processing, whereas Stone Soup is a Python project, providing class-based structures and programming interfaces to implement modules. The possibility of interface with Stone Soup could be an interesting direction for future work. The INSTEAD system has been previously presented in [7; 8], which illustrate different technical aspects of the system as follows. The first paper [7] contains more details on the techniques for video systems person tracking hand-over with experiments for later integration in the INSTEAD architecture. The second paper [8] contains more details about the INSTEAD deployment in DEXTER system and focuses on detection accuracy results at BCT. The technical details of the re-identification method are provided in other papers [23; 24].