Random forest based classification of retroreflective foils for visible light sensing of an indoor moving object

Systems based on visible light sensing can relief some of the anticipated challenges arising from the predicted massive increase in connected Internet of Thing devices. For example, identification and speed determination of mobile objects can be achieved without the necessity to place actively powered devices or sensors on the object itself. Instead, the surfaces of the objects are simply equipped (coded) with sequences of differently colored foils, which affect the respective spectral compositions of reflected light. In this work, we present an innovative approach for classifying differently colored retroreflective foils in varying size configurations on a moving object by utilizing the supervised machine learning algorithm of random forest. For the respective experimental setup, consisting of a single light source (as a transmitter) and a single RGB sensitive photodiode (as a receiver for the reflected light from the coded mobile object), we can show that not only the task of identification, but also the task of determining the speed of the object can be achieved with 98.8 % accuracy. By utilizing a minimal feature set to create the random forest, the proposed approach requires only minimal computational effort for model generation and classification. The therewith-achieved results are directly compared to an algorithm based on the more complex and resource demanding method of Euclidian distances. The satisfying congruence discloses the applicability of the random forest model for such tasks, especially in scenarios with highly limited memory resources and limited available computational performance.