## 4 Discussion  
In this study, we present a system that, based on the method of visible light sensing in combination with retroreflective foils, can perform the task of identification and speed estimation of a moving object without the need to place any actively powered components on the object itself. Basically, these tasks are fulfilled by means of acquiring the reflections caused by the differently colored foils in different size configurations and calculating features to be used in the supervised machine learning approach of random forest. In this work, we not only showed an advancement of the classification accuracy, compared to our previous work, on the base of a solution approach for which the spectral power distribution of the emitted light of the utilized LED was modified, but also that this advancement can be achieved for a larger distance between the light source and the object as it was applied in our previous work. Furthermore, we also expanded the number of the tasks to be fulfilled by our system by adding the task of determining the movement direction of the object itself, which was achieved with 100% correct results. Last, but not least, we also showed that the necessity of placing additional components (light barrier) alongside the tracks, as it was done in our previous work, can be overcome, which consequently simplifies the experimental setup. This simplification was achieved by placing an additional specular reflecting element on the train, which provides a clear trigger and additionally provides a possibility to reduce the number of features used in the random forest. Finally, we also expanded the scope of our experimental setup and demonstrated the classification accuracy under the presence of ambient light. With the help of a distinctive accentuation of the red spectral range in the spectral power distribution of the light emitted from the LED by a simple exchange of the LED from  
Figure 9: Scatter plot of the calculated mean values during the known absence of the train. a white LED to an RGBW LED with the same PCB footprint, we were able to improve the classification accuracy from 98.8% in our previous work to 99.96% correct classifications with the same setting (only LED) and 99.41% in the setting when also additional ambient light is present. It is clear that in real application scenarios, additional ambient light is inevitable, but as we showed with the results, even in the presence of ambient light (fluorescent tubes), we can achieve a higher classification accuracy than in our previous work where no ambient light was present. As already described, these improvements were achieved for a larger distance between the light source and the moving object, with an experimental setup that requires lower effort in its installation and with fewer features used in the random forest. Especially in regard to the increased distance between the light source and the moving object, we would like to point out that we did not follow the solution to increase the output power of the light source in order to generate the same illuminance on the reflective area of the moving object. In contrast, we were able to show that a better allocation of the emitted light spectrum improves the classification accuracy, whilst having a lower illuminance of 500 lux, compared to the 690 lux in our previous work. Nevertheless, as shown, there are some slight deteriorations in correct classifications when additional ambient light is introduced. First, it has to be pointed out that the applied ambient light, which is generated by a completely different type of light source (fluorescent tubes) provides a very challenging environment. The resulting spectrum impinging on the train consequently deteriorates the difference in the acquired reflections from the foils to a certain extent.