FPDM: Fisheye Panoptic segmentation dataset for Door Monitoring

Abstract

Most existing panoptic segmentation datasets are not suited for applications in the railway environment. This paper introduces a new dataset composed of video feeds taken in the vicinity of train doors. It is aimed at the training of deep learning algorithms to identify the obstacles between doors to ensure passenger safety during boarding and to reduce boarding time. The dataset is acquired from fisheye cameras located at the train doors. The data is annotated entirely manually. The Fisheye Panoptic Door Monitoring dataset (FPDM) contains 3952 images with their annotation masks featuring 18 of the most frequent instance categories in the vicinity of train doors. FPDM answers the panoptic segmentation challenge by offering a new challenging dataset for the computer vision community. We present detailed information on the process of acquisition, annotation, and division of the data into training and validation sets in addition with an evaluation of an existing deep learning method.