In recent years, Echo State Networks (ESNs), a special type of Recurrent Neural Networks (RNNs), have become increasingly established in the Machine Learning (ML) community due to their relatively simple initialization and training methods. Traditionally, the input and recurrent weights are generated randomly, with only the output weights being trained, typically using linear regression. However, recent publications have proposed alternative ways to initialize the weight matrices, e.g., by using more deterministic methods or data-driven approaches. This is the first work comparing different simple reservoir structures and an ESN with pre-trained input weights for the task of monitoring the state of a door using a surveillance camera in real-time. The results show that deterministic ESN structures perform better than the randomly initialized baseline, achieving a frame error rate of 2.62% vs. 2.93%.