Current navigational systems rarely consider generic road landmarks in their navigation instructions, which can lead to mistakes, frustration, and distraction. However, automatic detection of road landmarks is difficult, as current approaches to object detection focus either on out-of-context objects which have special characteristics or on very specific domains. This work presents a future direction for a user-friendly navigational system based on state-of-the-art computer vision techniques that use deep learning for object detection. We propose an automatic hierarchical approach for detecting and classifying a set of static and dynamic road landmarks that would be useful in automatic navigational systems. We further demonstrate a set of optimisations that improve performance and accuracy of the basic system. We evaluate our approach on a natural, 'in-the-wild' dataset to determine how well it handles natural automotive input. Finally, we demonstrate a use-case for our system that extracts information about a vehicle's location and intention.
['Olivia Wiles', 'Marwa Mahmoud', 'Peter Robinson', 'Eduardo Dias', 'Lee Skrypchuk']
Towards a User-Centric In-Vehicle Navigational System