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Published April 5, 2020 | Version Version 1
Dataset Restricted

Railway Pedestrian Dataset (RAWPED)

  • 1. Dokuz Eylul University
  • 2. KentKart R&D
  • 3. Yaşar University

Description

Data abstract:
This Zenodo upload contains the Railway Pedestrian Dataset (RAWPED) for benchmarking and developing pedestrian detection methods for on-board driver assistance systems. Training set includes sets of images in jpg format and their annotations in txt file format. Test set includes only sets of images in jpg format. (70% of images and annotations of each subset are in training set and 30% of images of each subset are in test set.)

Paper abstract:
Pedestrian Detection (PD) is one of the most studied issues of driver assistance systems. Although a tremendous effort is already given to create datasets and to develop classifiers for cars, studies about railway systems remain very limited. This paper shows that direct application of neither existing advanced object detectors (such as AlexNet, VGG, YOLO etc), nor specifically created systems for PD (such as Caltech/INRIA trained classifiers), can provide enough performance to overcome railway specific challenges. Fortunately, it is also shown that without waiting the collection of a mature dataset for railways as comprehensively diverse and annotated as the existing ones for cars, a Transfer Learning (TL) approach to fine-tune various successful deep models (pre-trained using both extensive image and pedestrian datasets) to railway PD tasks provides an effective solution. To achieve TL, a new RAilWay PEdestrian Dataset (RAWPED) is collected and annotated. Then, a novel three-stage system is designed. At its first stage, a feature-classifier fusion is created to overcome the localization and adaptation limitations of deep models. At the second stage, the complementarity of the transferred models and diversity of their results are exploited by conducted measurements and analyses. Based on the findings, at the third stage, a novel learning strategy is developed to create an ensemble, which conditionally weights the outputs of individual models and performs consistently better than its components. The proposed system is shown to achieve a log average miss rate of 0.34 and average precision of 0.93, which are significantly better than the performance of compared well-established models.

Attribution:
If you use this data set in your own work, please cite this paper:

  • Toprak, T., Belenlioglu, B., Aydin, B., Guzelis, C., & Selver, M. A. (2020). Conditional Weighted Ensemble of Transferred Models for Camera Based Onboard Pedestrian Detection in Railway Driver Support Systems. IEEE Transactions on Vehicular Technology.

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.

Request access

If you would like to request access to these files, please fill out the form below.

You need to satisfy these conditions in order for this request to be accepted:

This dataset is made available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

1. That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Dokuz Eylul University and KentKart R&D) do not accept any responsibility for errors or omissions.
2. That you include a reference to the RAWPED in any work that makes use of the dataset. For research papers, cite our publication as given below.
3. That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
4. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
5. That all rights not expressly granted to you are reserved by us (Dokuz Eylul University and KentKart R&D).

We can share the dataset to scholars for research purposes upon explicit request. Please drop us a line about your research, if you would like to obtain access to the dataset and we'll open it you to as quickly as possible. Please cite the original paper when using, repurposing or expanding our work:

Toprak, T., Belenlioglu, B., Aydin, B., Guzelis, C., & Selver, M. A. (2020). Conditional Weighted Ensemble of Transferred Models for Camera Based Onboard Pedestrian Detection in Railway Driver Support Systems. IEEE Transactions on Vehicular Technology.

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