Activity Classification of an Unmanned Aerial Vehicle Using Tsetlin Machine

The activity classification for aerial vehicles plays a vital role in privacy monitoring and security surveillance applications, which is crucial and valuable in modern times. This paper presents the Tsetlin Machine model for aerial vehicle’s activity classification using the mm-Wave frequency modulated continuous wave (FMCW) Radar data. The proposed Tsetlin Machine (TM) model is based on propositional logic, which is much more transparent and lighter than the existing models. It can also be easily transferred to hardware, making it more useful even in practical circumstances. Furthermore, the model has a 92.5% accuracy in activity classification, which is close to other lightweight classification models like logistic regression, light gradient boosting machine (GBM) and support vector machine (SVM). Furthermore, the proposed model’s accuracy is much better than the pre-trained models such as VGG16, ResNet50, and InceptionResNet with at least $98 \times$ reduction in memory size.