Szczegóły publikacji
Opis bibliograficzny
HiSS: human-inspired semantic segmentation for vehicle interior scene understanding / Aleksander KOSTUCH, Joanna JAWOREK-KORJAKOWSKA // W: ICCV-W 2025 [Dokument elektroniczny] : 2025 IEEE/CVF International Conference on Computer Vision Workshops : 19–20 October 2025, Honolulu, United States : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2025. — ( IEEE International Conference on Computer Vision Workshops ; ISSN 2473-9936 ). — Print on Demand (PoD) ISBN: 979-8-3315-8989-9. — e-ISBN: 979-8-3315-8988-2. — S. 4851–4860. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 4859–4860, Abstr. — Publikacja dostępna online od: 2026-02-23. — A. Kostuch - dod. afiliacja: Aptiv Technical Center, Kraków
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 167852 |
|---|---|
| Data dodania do BaDAP | 2026-06-16 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICCVW69036.2025.00503 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Conference on Computer Vision 2025 |
| Czasopismo/seria | IEEE International Conference on Computer Vision Workshops |
Abstract
Computer vision plays a critical role in autonomous vehicle technology, with semantic segmentation being a key component by labeling each pixel in an image. This study evaluates the effectiveness of a bio-inspired hybrid approach that combines binary and multiclass segmentation process to improve semantic segmentation in vehicle interiors as part of a Cabin Monitoring System, drawing inspiration from human visual perception processes. Experiments were conducted on synthetic data from the SVIRO dataset using SegFormer, a transformer-based model for segmentation, and YOLO for detection, respectively. Various architectures with a novel hybrid approach were tested that mimics the hierarchical processing of the human attention mechanism by using different Gaussian and distance transform-based masks to refine the focus of the multiclass model. The innovative hybrid approach combining binary and multiclass segmentation improved the mean Intersection over Union from 0.55 to 0.61. Two perceptually-motivated postprocessing methods were developed: a basic approach inspired by visual completion phenomena that further enhanced mIoU to 0.76, and an extended context-aware variant that achieved better accuracy (0.87 vs. 0.84) by analyzing neighborhood information to determine appropriate replacement classes, simi-lar to human contextual integration processes. These methods are highly generic and can be adapted to a wide range of segmentation challenges beyond vehicle interiors. The integration of human perceptual principles into computational vision systems demonstrates the value of biomimetic approaches in advancing artificial intelligence. In addition, we provide a detailed discussion of the results and outline potential directions for future research.
