Training of Convolutional Networks on Multiple Heterogeneous Datasets for Street Scene Semantic Segmentation

We propose a convolutional network with hierarchical classifiers for per-pixel semantic segmentation, which is able to be trained on multiple, heterogeneous datasets and exploit their semantic hierarchy. Our network is the first to be simultaneously trained on three different datasets from the intelligent vehicles domain, i.e. Cityscapes, GTSDB and Mapillary Vistas, and is able to handle different semantic level-of-detail, class imbalances, and different annotation types, i.e. dense per-pixel and sparse bounding-box labels. We assess our hierarchical approach, by comparing against flat, non-hierarchical classifiers and we show improvements in mean pixel accuracy of 13.0% for Cityscapes classes and 2.4% for Vistas classes and 32.3% for GTSDB classes. Our implementation achieves inference rates of 17 fps at a resolution of 520x706 for 108 classes running on a GPU.Comment: IEEE Intelligent Vehicles 201

Intelligent imaging system for optimal night time driving

In the recent era, vehicles become a need of public. According to Statistic Portal, in year 2018 alone, more than 81 million vehicles were sold. This results in a large number of vehicles commuting on roads, thus increases the risks of road users. Road safety is the paramount and joint responsibility of all road users, which include pedestrians and travellers using different means of transport. Safety is always a main concern for drivers. It is a complex and difficult task even for an experienced senior driver. Road accident is the most unwanted thing to happen to a road user; it was reported that most of the road users are familiar with the general rules and safety measures when using roads, nonetheless their carelessness are causing the accidents and crashes. Zhang et.al [1] proposed an intelligent driver assist system for urban driving. This system provided smart navigation for its users with intelligent parking assistance to improve driving comfort while ensure the safety of the driver. The investigations of the system performance showed high precisions in the determination of the traffic flow and parking availabilit