TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed)
A1  - Stahl, Bastian
A1  - Apfelbeck, Jürgen
A1  - Lange, Robert
T1  - Classification of Micromobility Vehicles in Thermal-Infrared Images Based on Combined Image and Contour Features Using Neuromorphic Processing
JF  - Applied Sciences
N2  - Trends of environmental awareness, combined with a focus on personal fitness and health, motivate many people to switch from cars and public transport to micromobility solutions, namely bicycles, electric bicycles, cargo bikes, or scooters. To accommodate urban planning for these changes, cities and communities need to know how many micromobility vehicles are on the road. In a previous work, we proposed a concept for a compact, mobile, and energy-efficient system to classify and count micromobility vehicles utilizing uncooled long-wave infrared (LWIR) image sensors and a neuromorphic co-processor. In this work, we elaborate on this concept by focusing on the feature extraction process with the goal to increase the classification accuracy. We demonstrate that even with a reduced feature list compared with our early concept, we manage to increase the detection precision to more than 90%. This is achieved by reducing the images of 160 × 120 pixels to only 12 × 18 pixels and combining them with contour moments to a feature vector of only 247 bytes.
KW  - neuromorphic processing
KW  - Machine vision
KW  - traffic data
KW  - urban planning
KW  - Machine Learning
KW  - long-wave infrared
KW  - micromobility
KW  - thermal imaging
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-66488
SN  - 2076-3417
SS  - 2076-3417
U6  - https://doi.org/10.3390/app13063795
DO  - https://doi.org/10.3390/app13063795
VL  - 13
IS  - 6
SP  - 18
S1  - 18
PB  - MDPI
CY  - Basel
ER  -