## 6 Conclusions  
In this paper, we demonstrated the significant improvement of our previous concept for a low-power multi-class micromobility vehicle counting system utilizing neuromorphic processing and low-resolution thermal imaging arrays. In field tests, we verified that the sensors are capable of performing under real-world conditions and that our concept for the set-up is feasible. We improved on both our hardware and software design with our prototype. By combining the features extracted from the image and the object's contour, we improved our classification accuracy (F1 score) to over 90% (compare with Figure 10) for certain classes and to an average of 87% (compare with Table 9). We used fully automated data segmentation algorithms, and we are confident that these approaches can be developed further to obtain a classification accuracy close to those of the hand-segmented approaches. With outdoor tests, we could demonstrate that the implemented concept is feasible.  
For future work, we suggest the following aspects to be considered:  
1. **Training data:** Most important for the next step in development is to conduct further testing in an outdoor environment, acquiring more data.
2. **Self-learning**: The current approach does not yet fully utilize the NM500's capability for self-learning. It could be used to allow the system to learn during operation and thus independently adapt to new environments.
3. **Multi-step classification**: In this work, we optimized the performance by combining features from different sources into one feature vector. It could also be feasible to perform multiple classifications using different feature sets and interpret the different results in combination.
4. **Utilization of the NM500**: The current task of the NM500 is just the final classification, as preprocessing, segmentation, and detection are still performed on the host system. It should be evaluated which tasks could also be performed by the NM500.
5. **Sensor fusion**: The new generation of our prototype utilizes a low-power radar as a trigger for the infrared sensors. It should be investigated how the information of both sensors could be used in a sensor fusion approach to further enhance the performance.  
In addition, the current concept of using radar information as a trigger and only reduced thermal still images may give away a lot of information potential. While evaluation of the complete LWIR full motion data flow does not comply with the power consumption requirements, using a sequence of at least multiple (3-5) frames could further improve the overall performance. For application scenarios allowing wall plug power, additional processing concepts might be considered, making use of the full motion data of the LWIR camera. Finally, different sensor sets could then be considered, including 3D time-of-flight cameras [16; 17]. Such systems have also shown promising results in a parallel development path of this project. These results will be presented in a separate publication.  
Writing--original draft preparation, B.S.; review and editing, R.L.; supervision, J.A. and R.L. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the Federal Ministry for Economic Affairs and Energy (BMWi) on the basis of a decision by the German Bundestag. Funding code: ZF4190305 GR9.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
The authors declare no conflict of interest.