# Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation

arXiv: https://arxiv.org/abs/1910.03858 · HTML: https://mlsanity.com/paper/1910.03858/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2019.

## Built on by (most cited first, 19 of 19)

- [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)
- [From SLAM to Situational Awareness: Challenges and Survey](https://mlsanity.com/paper/2110.00273/) (2021)
- [PIP-Net: Pedestrian Intention Prediction in the Wild](https://mlsanity.com/paper/2402.12810/) (2024)
- [UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning](https://mlsanity.com/paper/2308.09953/) (2023)
- [BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind](https://mlsanity.com/paper/2402.07402/) (2024)
- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)
- [GTransPDM: A Graph-embedded Transformer with Positional Decoupling for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2409.20223/) (2024)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [ACIT: Attention-Guided Cross-Modal Interaction Transformer for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2511.20020/) (2025)
- [Single Shot Multitask Pedestrian Detection and Behavior Prediction](https://mlsanity.com/paper/2101.02232/) (2021)
- [Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review](https://mlsanity.com/paper/2409.07645/) (2024)
- [BikeActions: An Open Platform and Benchmark for Cyclist-Centric VRU Action Recognition](https://mlsanity.com/paper/2601.10521/) (2026)
- [Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos](https://mlsanity.com/paper/2204.13548/) (2022)
- [HEViTPose: High-Efficiency Vision Transformer for Human Pose Estimation](https://mlsanity.com/paper/2311.13615/) (2023)
- [Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areas](https://mlsanity.com/paper/2402.10077/) (2024)
- [Reliable Heading Tracking for Pedestrian Road Crossing Prediction Using Commodity Devices](https://mlsanity.com/paper/2410.06400/) (2024)
- [Intention Recognition in Real-Time Interactive Navigation Maps](https://mlsanity.com/paper/2502.17581/) (2025)
- [Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2607.12293/) (2026)

## Abstract (arXiv)

Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.