Advanced driver assistance systems (ADAS) have attracted more and more attention nowadays, where various IT technologies are introduced to vehicles to enhance driving safety and automation.
MCL alumni, Junting Zhang and Yuhang Song, together with MediaTek Inc. have started a collaborative research project on ADAS-oriented deep learning technologies since January 2016. Single-image-based traffic scene segmentation and road detection have been studied extensively throughout 2016. We adapted the state-of-the-art general-purpose CNN architectures to urban scene semantic segmentation task, overcoming the cross-domain issue. On the other hand, computational and memory efficiency have always been our major concerns, we were also devoted to simplify the network structure and reduce redundant computation.
In 2017, we will explore the deep learning technologies for video processing. Although there are many interesting results in semantic urban scene understanding based on the CNN technology, semantic video understanding is still a challenging problem. We will try to find a semantic video understanding method that outperforms the single-image-based algorithms. To address this type of problems, we will exploit the temporal information.