Stone Soup open source framework for tracking and state estimation: enhancements and applications

Abstract

The tracking and state estimation community is broad, with diverse interests. These range from algorithmic research and development, applications to solve specific problems, to systems integration. Yet until recently, in contrast to similar communities, few tools for common development and testing were widespread. This was the motivation for the development of Stone Soup - the open source tracking and state estimation framework. The goal of Stone Soup is to conceive the solution of any tracking problem as a machine. This machine is built from components of varying degrees of sophistication for a particular purpose. The encapsulated nature and modularity of these components allow efficiency and reuse. Metrics give confidence in evaluation. The open nature of the code promotes collaboration. In April 2019, the Stone Soup initial beta version (v0.1b) was released, and though development continues apace, the framework is stable, versioned and subject to review. In this paper, we summarise the key features of and enhancements to Stone Soup - much advanced since the original beta release - and highlight several uses to which Stone Soup has been applied. These include a drone data fusion challenge, sensor management, target classification, and multi-object tracking in video using TensorFlow object detection. We also detail introductory and tutorial information of interest to a new user.