# A Dataset and Model for Crossing Indian Roads

arXiv: https://arxiv.org/abs/2211.07916 · HTML: https://mlsanity.com/paper/2211.07916/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Understanding the Effective Receptive Field in Deep Convolutional Neural Networks](https://mlsanity.com/paper/1701.04128/) (2017)
- [IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments](https://mlsanity.com/paper/1811.10200/) (2018)
- [Collaborative Spatio-temporal Feature Learning for Video Action Recognition](https://mlsanity.com/paper/1903.01197/) (2019)

## Built on by (most cited first, 2 of 2)

- [Safe Road-Crossing by Autonomous Wheelchairs: a Novel Dataset and its Experimental Evaluation](https://mlsanity.com/paper/2403.08984/) (2024)
- [Experimental Evaluation of Road-Crossing Decisions by Autonomous Wheelchairs against Environmental Factors](https://mlsanity.com/paper/2406.18557/) (2024)

## Abstract (arXiv)

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. The project webpage is http://roadcross-assistant.github.io/Website/.