# The EuroCity Persons Dataset: A Novel Benchmark for Object Detection

arXiv: https://arxiv.org/abs/1805.07193 · HTML: https://mlsanity.com/paper/1805.07193/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2018.

## Built on by (most cited first, 30 of 57)

- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
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- [Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines](https://mlsanity.com/paper/2406.14482/) (2024)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection](https://mlsanity.com/paper/2106.05499/) (2021)
- [Towards Domain Generalization in Object Detection](https://mlsanity.com/paper/2203.14387/) (2022)
- [Anomaly Detection in Multi-Agent Trajectories for Automated Driving](https://mlsanity.com/paper/2110.07922/) (2021)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline](https://mlsanity.com/paper/2206.00527/) (2022)
- [Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond](https://mlsanity.com/paper/2201.03176/) (2022)
- [Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving](https://mlsanity.com/paper/2307.14889/) (2023)
- [Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2308.02935/) (2023)
- [F2DNet: Fast Focal Detection Network for Pedestrian Detection](https://mlsanity.com/paper/2203.02331/) (2022)
- [Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments](https://mlsanity.com/paper/2403.13803/) (2024)
- [E-Scooter Rider Detection and Classification in Dense Urban Environments](https://mlsanity.com/paper/2205.10184/) (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)
- [RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection](https://mlsanity.com/paper/2508.13878/) (2025)
- [Robustness in Compressed Neural Networks for Object Detection](https://mlsanity.com/paper/2102.05509/) (2021)
- [Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics](https://mlsanity.com/paper/2303.04302/) (2023)
- [Exploring the Potential of Multi-Modal AI for Driving Hazard Prediction](https://mlsanity.com/paper/2310.04671/) (2023)
- [Semi-supervised Text-based Person Search](https://mlsanity.com/paper/2404.18106/) (2024)
- [Ambiguous Annotations: When is a Pedestrian not a Pedestrian?](https://mlsanity.com/paper/2405.08794/) (2024)
- [RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety](https://mlsanity.com/paper/2102.11730/) (2021)
- [SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset](https://mlsanity.com/paper/2208.02580/) (2022)
- [Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving](https://mlsanity.com/paper/2312.06306/) (2023)
- [Real-time Traffic Object Detection for Autonomous Driving](https://mlsanity.com/paper/2402.00128/) (2024)
- [XS-VID: A Large-Scale Benchmark for Small Object Detection and Tracking in Videos](https://mlsanity.com/paper/2407.18137/) (2024)

## Abstract (arXiv)

Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238200 person instances manually labeled in over 47300 images, EuroCity Persons is nearly one order of magnitude larger than person datasets used previously for benchmarking. The dataset furthermore contains a large number of person orientation annotations (over 211200). We optimize four state-of-the-art deep learning approaches (Faster R-CNN, R-FCN, SSD and YOLOv3) to serve as baselines for the new object detection benchmark. In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the effect of the training set size, the dataset diversity (day- vs. night-time, geographical region), the dataset detail (i.e. availability of object orientation information) and the annotation quality on the detector performance. Finally, we analyze error sources and discuss the road ahead.