Composition

The ECP dataset. Focus on Persons in Urban Traffic Scenes.

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. Diversity is gained by recording this dataset throughout Europe.

Statistics

Person annotations.To enable your classifier.

All objects were annotated with tight bounding boxes delineating their full extent. If objects were partly occluded, their full extents were estimated (this is useful for later processing steps such as tracking) and the level of occlusion was annotated.

Object Class # objects (day) # objects (night) # objects (sum)
Pedestrian 183004 35309 218313
Rider 18216 1564 19780

Part-based annotations.To improve your classifier.

For riders, we labeled the riding person and its ride-vehicle with two separate bounding boxes, and annotated the ride-vehicle type. Riderless-vehicles of the same type in close proximity were captured by one class- specific group box (e.g. several bicycles on a rack).

Object Class # objects (day) # objects (night) # objects (sum)
Bicycle 9666 614 10280
Motorbike 2196 229 2425
Scooter 5748 683 6431
Tricycle 94 5 99
Wheelchair 125 4 129
Buggy 322 26 348
Co-Rider 671 137 808

Ignore regions.To boost your training.

A person is annotated with a rectangular (class-specific) ignore region if a person is smaller than 20 px, if there are doubts that an object really belongs to the appropriate class, and if instances of a group can not be discriminated properly. In the latter case, several instances may be grouped inside a single ignore region.

Object Class # objects (day) # objects (night) # objects (sum)
Pedestrian 73449 19857 93306
Rider+Vehicle 2224 175 2399
Bicycle 21446 1779 23225
Motorbike 4817 318 5135
Scooter 12438 748 13186
Tricycle 89 10 99
Buggy 1141 109 1250
Wheelchair 16 4 20