EuroCity Persons: A Novel Benchmark for Person Detection in Traffic Scenes

By Markus Braun, Sebastian Krebs, Fabian B. Flohr, Dariu M. Gavrila. 2018

262 citations recorded by the source corpus.

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 238,200 person instances manually labeled in over 47,300 images, EuroCity Persons is nearly one order of magnitude larger than datasets used previously for person detection in traffic scenes. The dataset furthermore contains a large number of person orientation annotations (over 211,200). 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- versus 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.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019, background): “In the recent EuroCity persons dataset [5], the authors argued that any anonymization measure will introduce a bias.”
VEGA: Towards an End-to-End Configurable AutoML Pipeline (2020, methodology): “We evaluate this algorithm on dataset ECP (Braun et al., 2019) with the metric log average miss-rate (LAMR).”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “The recent Eurocity dataset [87] is the most diverse dataset we have reviewed.”
A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving (2020, methodology): “[107] 2019 C EuroCity Persons [126] E+A MC-dropout, Direct modeling NMS Log Average Miss Rate”
Towards Large-Scale Small Object Detection: Survey and Benchmarks (2022, background): “Instead of restricting our scope to speciﬁc tasks, we investigate the related datasets which span over a wide range of research areas, including face detection [8], pedestrian detection [7], [148], [149], object detection in aerial images [20], [30], [160], [162], to name a few.”