|Postgraduate Student in the Department of Computer Science||Mathematical Sciences and Computer Science Building||+44 (0) 191 33 41736|
Naif Alshammari is PhD student working on the challenging topic of automotive scene understanding under extreme variations in environmental conditions such as varying illumination and adverse weather. His research makes use of a range of varying approaches, spanning both illumination invariant image transformation and contemporary convolutional network approaches within the challenging real-time demands of automotive computer vision to support future vehicle autonomy. His aims to address key sensing challenges aimed towards future all-condition (i.e. illumination and weather invariant) road vehicle autonomy in terms of both effective semantic scene understanding and 3D environmental awareness under such adverse conditions.
- Computer Vision (semantic segmentation and depth estimation)
- Machine Learning (deep learning)
- Alshammari, N., Akcay, S. & Breckon P., T. (Submitted), Multi-Task Learning for Automotive Foggy Scene Understanding via Domain Adaptation to an Illumination-Invariant Representation, Submitted to IRCA 2020.
- Alshammari, N., Akcay, S. & Breckon, T. (2018), On the Impact of Illumination-Invariant Image Pre-transformation for Contemporary Automotive Semantic Scene Understanding, 29th IEEE Intelligent Vehicles Symposium (IEEE IV 2018). Changshu, Suzhou, China, IEEE, Piscataway, 1027-1032.