Crossroad Camera Dataset - Mobility Aid Users
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The most vulnerable group of traffic participants are pedestrians using mobility aids. While there has been significant progress in the robustness and reliability of camera based general pedestrian detection systems, pedestrians reliant on mobility aids are highly underrepresented in common datasets for object detection and classification. To bridge this gap and enable research towards robust and reliable detection systems which may be employed in traffic monitoring, scheduling, and planning, we present this dataset of a pedestrian crossing scenario taken from an elevated traffic monitoring perspective together with ground truth annotations (Yolo format [1]). Classes present in the dataset are pedestrian (without mobility aids), as well as pedestrians using wheelchairs, rollators/wheeled walkers, crutches, and walking canes. The dataset comes with official training, validation, and test splits. An in-depth description of the dataset can be found in [2]. If you make use of this dataset in your work, research or publication, please cite this work as: @inproceedings{mohr2023mau,author = {Mohr, Ludwig and Kirillova, Nadezda and Possegger, Horst and Bischof, Horst},title = {{A Comprehensive Crossroad Camera Dataset of Mobility Aid Users}},booktitle = {Proceedings of the 34th British Machine Vision Conference ({BMVC}2023)},year = {2023}} Archive mobility.zip contains the full detection dataset in Yolo format with images, ground truth labels and meta data, archive mobility_class_hierarchy.zip contains labels and meta files (Yolo format) for training with class hierarchy using e.g. the modified version of Yolo v5/v8 available under [3].To use this dataset with Yolo, you will need to download and extract the zip archive and change the path entry in dataset.yaml to the directory where you extracted the archive to. [1] https://github.com/ultralytics/ultralytics[2] coming soon[3] coming soon
交通参与者中最弱势的群体为使用移动辅助器具的行人。尽管基于摄像头的通用行人检测系统在鲁棒性与可靠性方面已取得显著进展，但在常见的目标检测与分类数据集内，依赖移动辅助器具的行人占比极低。为填补这一研究空白，支撑可应用于交通监测、调度与规划的鲁棒可靠检测系统的研发，我们发布本数据集：其采集自高架交通监测视角的行人过街场景，并附带Yolo格式[1]的真值标注。数据集包含的类别为：未使用移动辅助器具的行人，以及使用轮椅、助行架/轮式助行器、拐杖与手杖的行人。本数据集附带官方划分的训练集、验证集与测试集。关于该数据集的详细描述可参见文献[2]。若您在工作、研究或论文发表中使用本数据集，请按以下格式引用本成果：@inproceedings{mohr2023mau,author = {Mohr, Ludwig and Kirillova, Nadezda and Possegger, Horst and Bischof, Horst},title = {{A Comprehensive Crossroad Camera Dataset of Mobility Aid Users}},booktitle = {Proceedings of the 34th British Machine Vision Conference ({BMVC}2023)},year = {2023}} 归档文件mobility.zip包含Yolo格式的完整检测数据集，内含图像、真值标签与元数据；归档文件mobility_class_hierarchy.zip包含用于结合类别层级训练的标签与元文件（Yolo格式），例如可使用文献[3]中提供的修改版Yolo v5/v8。若需结合Yolo使用本数据集，请下载并解压该zip归档文件，并将dataset.yaml中的路径项修改为您解压归档的目标目录。[1] https://github.com/ultralytics/ultralytics[2] 即将上线[3] 即将上线
