# Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads

arXiv: https://arxiv.org/abs/2109.05226 · HTML: https://mlsanity.com/paper/2109.05226/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 11 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments](https://mlsanity.com/paper/1811.10200/) (2018)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction](https://mlsanity.com/paper/2002.08945/) (2020)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)
- [Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking](https://mlsanity.com/paper/1712.07920/) (2017)
- [DeepSignals: Predicting Intent of Drivers Through Visual Signals](https://mlsanity.com/paper/1905.01333/) (2019)
- [City-Scale Road Audit System using Deep Learning](https://mlsanity.com/paper/1811.10210/) (2018)

## Built on by (most cited first, 3 of 3)

- [Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads](https://mlsanity.com/paper/2204.08364/) (2022)
- [Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities](https://mlsanity.com/paper/2503.02967/) (2025)
- [EG-ARSA: An Expert-Grounded Open Model for Visual Road Safety Auditing in Low-Resource Settings](https://mlsanity.com/paper/2608.23563/) (2026)

## Abstract (arXiv)

Conventional approaches for addressing road safety rely on manual interventions or immobile CCTV infrastructure. Such methods are expensive in enforcing compliance to traffic rules and do not scale to large road networks. This paper proposes a simple mobile imaging setup to address several common problems in road safety at scale. We use recent computer vision techniques to identify possible irregularities on roads, the absence of street lights, and defective traffic signs using videos from a moving camera-mounted vehicle. Beyond the inspection of static road infrastructure, we also demonstrate the mobile imaging solution's applicability to spot traffic violations. Before deploying our system in the real-world, we investigate the strengths and shortcomings of computer vision techniques on thirteen condition-based hierarchical labels. These conditions include different timings, road type, traffic density, and state of road damage. Our demonstrations are then carried out on 2000 km of unconstrained road scenes, captured across an entire city. Through this, we quantitatively measure the overall safety of roads in the city through carefully constructed metrics. We also show an interactive dashboard for visually inspecting and initiating action in a time, labor and cost-efficient manner. Code, models, and datasets used in this work will be publicly released.