# Action and intention recognition of pedestrians in urban traffic

arXiv: https://arxiv.org/abs/1810.09805 · HTML: https://mlsanity.com/paper/1810.09805/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 10 of 10)

- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction](https://mlsanity.com/paper/2109.00953/) (2021)
- [Assessing Cross-dataset Generalization of Pedestrian Crossing Predictors](https://mlsanity.com/paper/2201.12626/) (2022)
- [Do Pedestrians Pay Attention? Eye Contact Detection in the Wild](https://mlsanity.com/paper/2112.04212/) (2021)
- [Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion](https://mlsanity.com/paper/2203.02489/) (2022)
- [On-Board Pedestrian Trajectory Prediction Using Behavioral Features](https://mlsanity.com/paper/2210.11999/) (2022)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control](https://mlsanity.com/paper/2104.03150/) (2021)
- [TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations](https://mlsanity.com/paper/2508.19866/) (2025)
- [Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos](https://mlsanity.com/paper/2204.13548/) (2022)

## Abstract (arXiv)

Action and intention recognition of pedestrians in urban settings are challenging problems for Advanced Driver Assistance Systems as well as future autonomous vehicles to maintain smooth and safe traffic. This work investigates a number of feature extraction methods in combination with several machine learning algorithms to build knowledge on how to automatically detect the action and intention of pedestrians in urban traffic. We focus on the motion and head orientation to predict whether the pedestrian is about to cross the street or not. The work is based on the Joint Attention for Autonomous Driving (JAAD) dataset, which contains 346 videoclips of various traffic scenarios captured with cameras mounted in the windshield of a car. An accuracy of 72% for head orientation estimation and 85% for motion detection is obtained in our experiments.