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Deep Classification-driven Domain Adaptation for Cross-Modal Driver Behavior Recognition

Reiß, Simon ORCID iD icon; Roitberg, Alina; Haurilet, Monica; Stiefelhagen, Rainer ORCID iD icon

Abstract (englisch):

We encounter a wide range of obstacles when integrating computer vision algorithms into applications inside the vehicle cabin, eg variations in illumination, sensor-type and-placement. Thus, designing domain-invariant representations is crucial for employing such models in practice. Still, the vast majority of driver activity recognition algorithms are developed under the assumption of a static domain, ie an identical distribution of training-and test data. In this work, we aim to bring driver monitoring to a setting, where domain shifts can occur at any time and explore generative models which learn a shared representation space of the source and target domain. First, we formulate the problem of unsupervised domain adaptation for driver activity recognition, where a model trained on labeled examples from the source domain (ie color images) is intended to adjust to a different target domain (ie infrared images) where only unlabeled data is available during training. To address this problem, we leverage current progress in imageto-image translation and adopt multiple strategies for learning a joint latent space of the source and target distribution and a mapping function to the domain of interest. ... mehr

DOI: 10.1109/IV47402.2020.9304782
Zitationen: 13
Zitationen: 15
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator KITopen-ID: 1000126799
Erschienen in 31st IEEE Intelligent Vehicles Symposium, IV 2020, Virtual, Las Vegas, United States, 19 October 2020 through 13 November 2020
Veranstaltung 31st IEEE Intelligent Vehicles Symposium (IV 2020), Online, 19.10.2020 – 13.11.2020
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 1042-1047
Bemerkung zur Veröffentlichung Die Veranstaltung fand wegen der Corona-Pandemie als Online-Event statt.
Nachgewiesen in Scopus
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