@article{AxenieHalilovMainetal.2025, author = {Axenie, Cristian and Halilov, Ertan and Main, Julian and Weiss, David}, title = {Edge neuro-statistical learning for event-based visual motion detection and tracking in roadside safety systems}, series = {Neuromorphic Computing and Engineering}, journal = {Neuromorphic Computing and Engineering}, publisher = {IOP Publishing}, issn = {2634-4386}, doi = {10.1088/2634-4386/adcbcb}, year = {2025}, abstract = {The Vision Zero Program's purpose is to reduce traffic-related fatalities and serious injuries while promoting equitable, safe, and healthy mobility for all. Ultimately, the challenge is to detect pedestrians during the day and especially at night to implement safety measures. The current study introduces an award-winning low-power solution employing neuromorphic visual sensing and hybrid neuro-statistical processing developed by the Technische Hochschule N{\"u}rnberg team for the TinyML Vision Zero San Jose Competition. The solution proposes a novel neuromorphic edge fusion of spiking neural networks and event-based expectation maximization for the detection and tracking of pedestrians and bicyclists. We provide a deployment-ready evaluation of the detection performance along with robustness, energy footprint, and weatherization while emphasizing the advantages of the neuro-statistical edge solution and its city-level scaling capabilities.}, language = {en} }