# Is the Pedestrian going to Cross? Answering by 2D Pose Estimation

arXiv: https://arxiv.org/abs/1807.10580 · HTML: https://mlsanity.com/paper/1807.10580/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2018.

## Built on by (most cited first, 25 of 25)

- [ROAD: The ROad event Awareness Dataset for Autonomous Driving](https://mlsanity.com/paper/2102.11585/) (2021)
- [Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attention](https://mlsanity.com/paper/2104.05485/) (2021)
- [Behavioral Intention Prediction in Driving Scenes: A Survey](https://mlsanity.com/paper/2211.00385/) (2022)
- [Coupling Intent and Action for Pedestrian Crossing Behavior Prediction](https://mlsanity.com/paper/2105.04133/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction](https://mlsanity.com/paper/2109.00953/) (2021)
- [GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction](https://mlsanity.com/paper/2311.14786/) (2023)
- [PIP-Net: Pedestrian Intention Prediction in the Wild](https://mlsanity.com/paper/2402.12810/) (2024)
- [Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings](https://mlsanity.com/paper/2304.08260/) (2023)
- [On-Board Pedestrian Trajectory Prediction Using Behavioral Features](https://mlsanity.com/paper/2210.11999/) (2022)
- [Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction](https://mlsanity.com/paper/2401.06757/) (2024)
- [Pedestrian Intention Prediction via Vision-Language Foundation Models](https://mlsanity.com/paper/2507.04141/) (2025)
- [WatchPed: Pedestrian Crossing Intention Prediction Using Embedded Sensors of Smartwatch](https://mlsanity.com/paper/2208.07441/) (2022)
- [Context-aware Multi-task Learning for Pedestrian Intent and Trajectory Prediction](https://mlsanity.com/paper/2407.17162/) (2024)
- [Multi-Context Fusion Transformer for Pedestrian Crossing Intention Prediction in Urban Environments](https://mlsanity.com/paper/2511.20011/) (2025)
- [SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving](https://mlsanity.com/paper/2306.03538/) (2023)
- [Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings](https://mlsanity.com/paper/2404.09574/) (2024)
- [Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review](https://mlsanity.com/paper/2409.07645/) (2024)
- [Occlusion-Aware Diffusion Model for Pedestrian Intention Prediction](https://mlsanity.com/paper/2511.00858/) (2025)
- [Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation](https://mlsanity.com/paper/2609.13328/) (2026)
- [High Efficiency Pedestrian Crossing Prediction](https://mlsanity.com/paper/2204.01862/) (2022)
- [MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction](https://mlsanity.com/paper/2409.09446/) (2024)
- [Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferability](https://mlsanity.com/paper/2412.03689/) (2024)
- [Temporal-contextual Event Learning for Pedestrian Crossing Intent Prediction](https://mlsanity.com/paper/2504.06292/) (2025)
- [Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification](https://mlsanity.com/paper/2609.07420/) (2026)

## Abstract (arXiv)

Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-of-the-art results.