5 juin 2018 EuroCity Persons dataset which provides a large number of highly diverse
Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper we
Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper we
Finally EuroCity Persons (ECP) [4] is a new pedestrian detection dataset
11 juil. 2019 In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We ...
29 juin 2021 Of the CityPersons and EuroCity Persons datasets our proposed method ... Caltech pedestrian dataset [8]
1 mai 2020 ImageNet and EuroCity Persons datasets respectively
In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the
this paper we enhance the recently released EuroCity Persons detection dataset
4 mars 2022 and Euro City Person datasets respectively when trained on a ... occlusion setting of Caltech Pedestrian and City Persons datasets.
Abstract—3D localization of persons from a single image is a challenging problem where advances are largely data-driven In this paper we enhance the recently released EuroCity Persons detection dataset a large and diverse automotive dataset cover- ing pedestrians and riders
Finally EuroCity Persons(ECP) [4] is anew pedestrian detection dataset which surpasses Caltechand CityPersons in terms of diversity and dif?culty Itis recorded in 31 different cities across 12 countries inEurope It has images for both day and night-time (thusreferred to as ECP day-time and ECP night-time)
The EuroCity Persons Dataset itself is distributed under the following License: Definitions ECP Dataset: all sensor and auxiliary data (e g annotations) pertaining to the EuroCity Persons Dataset as described on its official website hosted by Delft University of Technology
This paper presents an experimental study on 3D person localization (i e pedestrians cyclists) in traf?c scenes using monocular vision and LiDAR data We consider two 3D ob- ject detection methods PointPillars [6] and AVOD [7] which are among the top performers on the KITTI benchmark see ?g 1