CV Jawahar, a computer vision scientist along with his team at IIIT- Hyderabad, readied the world’s first public dataset of Indian driving conditions. This dataset not only aims to help the computer vision researchers from across the world to train their algorithms on Indian driving conditions but also lends road safety solutions.
The team that comprises of 35 members has been using images and videos that are captured by simple cameras which are attached to the vehicles. Cameras are more affordable than the existing sensors. Commenting on the same, Jawahar adds, “People would like to eventually move to video or image-based navigation as they are cheaper. We want solutions that are cheap and affordable to democratise this data.”
The team has been driving around and collecting drive sequences and then annotating them using a computer vision algorithm as the idea is to label signposts, pedestrians, types of vehicles, street lights, etc. The idea was to create a dataset that’s double the size of Cityscape, the biggest publicly available dataset at the time. Using over 180 drive sequences, the team has created about 10,000 pixel-level annotated images and 50,000 object level annotated images.
Cityscape has a dataset of about 5,000 frames annotated at the pixel level for 50 cities. Pixel-level annotation means each pixel in the image is associated to an object class such as road, rider, guardrail, car, truck, bus, sky and so on.