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.
The increased usage of unmanned aerial devices for commercial usage, such as drones, has presented a new challenge in aircraft security and overall public safety. Therefore, there is an urgent need to accurately detect and track drones. The objective of this paper is to classify rotary drones and fixed wing drones based on their trajectories. In order to develop classification models, Stone Soup open-source software framework is used to generate simulated track data using the location information of the drones available through Global Position System (GPS) telemetry data. Stone Soup can be used to study the quality of the tracks when classifying drones. To study the performance of the various classification methods in a realistic environment, false alarms were generated along with the tracker outputs. Tracker output is segmented into sub-trajectories and were used as inputs to the different classification models. Traditional machine learning algorithms namely Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Decision Trees (DT) and a deep learning algorithm namely Convolutional Neural Networks (CNN) were considered for developing classification models. Kinematic features derived from the sub-trajectories were used as features for machine learning algorithms while images obtained from the sub-trajectories were used as input to the CNN. In order to handle class imbalances, data augmentation was used. The performance of the various classification models validated the objective of this paper.
Python State Estimation and Modeling Library, pystemlib, is a library that implements Bayesian State Estimation theory for modeling and tracking target objects. This library was developed to overcome the limitations associated with licensed programming languages as well as imperative and numerical matrix-based programming styles that were used in previously developed libraries. pystemlib incorporates object-oriented, functional, and symbolic programming to develop accurate and easy-to-use tracking filters and models. This library is also capable of mapping state estimation results onto the geographical areas to which they correspond. Future work on this library will include optimizing the algorithms for speed and extending the library to incorporate multi-target tracking, data fusion, and image and video processing.
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