# SEPose: A Synthetic Event-based Human Pose Estimation Dataset for Pedestrian Monitoring

arXiv: https://arxiv.org/abs/2507.11910 · HTML: https://mlsanity.com/paper/2507.11910/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 31 references that are in the map.
- First posted on arXiv 16 Jul 2025.

## Builds on (most cited first, 22 of 22)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception](https://mlsanity.com/paper/1801.10202/) (2018)
- [The ApolloScape Open Dataset for Autonomous Driving and its Application](https://mlsanity.com/paper/1803.06184/) (2018)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [Recurrent Vision Transformers for Object Detection with Event Cameras](https://mlsanity.com/paper/2212.05598/) (2022)
- [DDD17: End-To-End DAVIS Driving Dataset](https://mlsanity.com/paper/1711.01458/) (2017)
- [Recent Event Camera Innovations: A Survey](https://mlsanity.com/paper/2408.13627/) (2024)
- [STCrowd: A Multimodal Dataset for Pedestrian Perception in Crowded Scenes](https://mlsanity.com/paper/2204.01026/) (2022)
- [Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning](https://mlsanity.com/paper/2404.05218/) (2024)
- [D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios](https://mlsanity.com/paper/1904.01975/) (2019)
- [eTraM: Event-based Traffic Monitoring Dataset](https://mlsanity.com/paper/2403.19976/) (2024)
- [SEVD: Synthetic Event-based Vision Dataset for Ego and Fixed Traffic Perception](https://mlsanity.com/paper/2404.10540/) (2024)
- [Crowd Saliency Detection via Global Similarity Structure](https://mlsanity.com/paper/1410.3756/) (2014)
- [WatchPed: Pedestrian Crossing Intention Prediction Using Embedded Sensors of Smartwatch](https://mlsanity.com/paper/2208.07441/) (2022)
- [2D Human Pose Estimation with Explicit Anatomical Keypoints Structure Constraints](https://mlsanity.com/paper/2212.02163/) (2022)
- [CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis](https://mlsanity.com/paper/2409.19942/) (2024)
- [Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment](https://mlsanity.com/paper/2407.00446/) (2024)

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

- [SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation](https://mlsanity.com/paper/2508.17643/) (2025)
- [eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers](https://mlsanity.com/paper/2601.06647/) (2026)

## Abstract (arXiv)

Event-based sensors have emerged as a promising solution for addressing challenging conditions in pedestrian and traffic monitoring systems. Their low-latency and high dynamic range allow for improved response time in safety-critical situations caused by distracted walking or other unusual movements. However, the availability of data covering such scenarios remains limited. To address this gap, we present SEPose -- a comprehensive synthetic event-based human pose estimation dataset for fixed pedestrian perception generated using dynamic vision sensors in the CARLA simulator. With nearly 350K annotated pedestrians with body pose keypoints from the perspective of fixed traffic cameras, SEPose is a comprehensive synthetic multi-person pose estimation dataset that spans busy and light crowds and traffic across diverse lighting and weather conditions in 4-way intersections in urban, suburban, and rural environments. We train existing state-of-the-art models such as RVT and YOLOv8 on our dataset and evaluate them on real event-based data to demonstrate the sim-to-real generalization capabilities of the proposed dataset.