Introduction
Roads in medium-sized Indian towns often have lots of traffic but no (or disregarded) traffic stops. This makes it hard for the
blind to cross roads safely, because vision is crucial to determine when crossing is safe. Automatic and reliable image-based safety
classifiers thus have the potential to help the blind to cross Indian roads. Yet, we currently lack datasets collected on Indian roads
from the pedestrian point-of-view, labelled with road crossing safety information. Existing classifiers from other countries are often
intended for crossroads, and hence rely on the detection and presence of traffic lights, which is not applicable in Indian conditions.
We introduce INDRA (INdian Dataset for RoAd crossing), the first dataset capturing videos of Indian roads from the pedestrian
point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and
vehicle bounding boxes. We train various classifiers to predict road crossing safety on this data, ranging from SVMs to convolutional
neural networks (CNNs). The best performing model DilatedRoadCrossNet is a novel single-image architecture tailored for deployment on
the Nvidia Jetson Nano. It achieves 79% recall at 90% precision on unseen images. Lastly, we present a wearable road crossing assistant
running DilatedRoadCrossNet, which can help the blind cross Indian roads in real-time.
Github repository Paper
Achievements
Awarded Best Paper (Indian Context) at ICVGIP 2022.
Find the arXiv pre-print here.
Awarded financial assistance
under the Innovation track of the Govt. of Gujarat’s
Startups & Innovation Policy.
Received AWS credits for this project from AWS
Machine Learning Hero
Mike Chambers through his AWS Machine Learning Competition on
LinkedIn.
Workflow
Oral Presentation at ICVGIP 2022