Image dataset comprising objects and obstacles in outdoor mobility of people with visual impairment

 There exist several commonly used datasets in relation to object detection that include COCO (with multiple versions) and ImageNet containing large annotations for 80 and 1000 objects (i.e. classes) respectively. However, very limited datasets are available comprising specific objects identified by visually imapeired people (VIP) such as wheel-bins, trash-Bags, e-Scooters, advertising boards, and bollard. Furthermore, the annotations for these objects are not available in existing sources.We identified a publically available 3D-scan dataset (without annotations) comprising variety of required objects including benches, advertising boards, pole, and wheel-bins [1]. The 3D-scans were captured using R-GBD camera from varying perspectives, orientations, distances, and angles producing more natural representations of data diversity as compared to augmented data generation (such as zoom in/out, translation, rotation, shear etc.). We transformed 3D-Scans of required objects (bins, advert-Boards, poles, and benches) to corresponding image frames.For the trash-Bags and e-Scooters, we used publically available google images (with NY-CC license). The cars and persons annotated datasets are acquired from public sources [2] and [3], respectively. We then annotate the images using public annotation tool (DarkLabel: https://darkpgmr.tistory.com/16) in the required form (bounding boxes, class label) to be used for the object detection.  [1] Q.-Y. Z. S. M. V. K. Sungjoon Choi, “A Large Dataset of Object Scans,” arXiv, 2016.  [2] M. S. J. D. L. F.-F. Jonathan Krause, “3D Object Representations for Fine-Grained Categorization,” in 4th IEEE Workshop on 3D Representation and Recognition, ICCV (3dRR-13), Sydney, Australia, 2013.  [3] P. L. C. C. L. X. T. Yubin DENG, “Pedestrian Attribute Recognition At Far Distance,” in Proceedings of the 22nd ACM international conference on Multimedia, 2014.  

当前目标检测领域常用的数据集多包含多版本COCO数据集，以及分别针对80类和1000类物体配备大规模标注信息的ImageNet数据集。然而，面向视障人群（Visually Impaired People, VIP）所关注的轮式垃圾桶、垃圾袋、电动滑板车、广告牌及路桩等特定物体的专用数据集却极为稀缺，且现有公开资源中也缺乏针对这类物体的标注数据。
我们发现了一个公开可用的3D扫描数据集（无标注信息），涵盖了长椅、广告牌、杆状物及轮式垃圾桶等多种目标所需物体[1]。该数据集通过RGB-D相机（R-GBD camera）从不同视角、方位、距离与角度采集3D扫描数据，相较于通过数据增强（如缩放、平移、旋转、剪切等）生成的人工样本，能更自然地展现数据分布的多样性。
我们将所需物体（轮式垃圾桶、广告牌、杆状物及长椅）的3D扫描模型转换为对应的图像帧。针对垃圾袋与电动滑板车，我们采用了持有NY-CC许可的公开谷歌图像数据。而汽车与行人标注数据集则分别来自公开资源[2]与[3]。随后，我们使用公开标注工具DarkLabel（网址：https://darkpgmr.tistory.com/16）对所有图像进行标注，生成目标检测任务所需的边界框（bounding box）与类别标签信息。
[1] Q.-Y. Z. S. M. V. K. Sungjoon Choi, 《大规模物体扫描数据集》, arXiv, 2016.
[2] M. S. J. D. L. F.-F. Jonathan Krause, 《面向细粒度分类的3D物体表征》, 发表于2013年澳大利亚悉尼举办的第4届IEEE 3D表征与识别研讨会（ICCV 3dRR-13）.
[3] P. L. C. C. L. X. T. Yubin DENG, 《远距离行人属性识别》, 发表于2014年第22届ACM多媒体国际会议论文集.