IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

By Girish Varma, Anbumani Subramanian, Anoop M. Namboodiri, Manmohan Chandraker, C. V. Jawahar. 2018

402 citations recorded by the source corpus.

While several datasets for autonomous navigation have become available in recent years, they have tended to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strong adherence to traffic rules. We propose DS, a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes. It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity. Consistent with real driving behaviors, it also identifies new classes such as drivable areas besides the road. We propose a new four-level label hierarchy, which allows varying degrees of complexity and opens up possibilities for new training methods. Our empirical study provides an in-depth analysis of the label characteristics. State-of-the-art methods for semantic segmentation achieve much lower accuracies on our dataset, demonstrating its distinction compared to Cityscapes. Finally, we propose that our dataset is an ideal opportunity for new problems such as domain adaptation, few-shot learning and behavior prediction in road scenes.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Evaluation & Benchmarks, Robotics & Control

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Citations in the corpus

Language-driven Semantic Segmentation (2022, background): “…of 7 different datasets (Lambert et al., 2020), including ADE20K (Zhou et al., 2019), BDD (Yu et al., 2020), Cityscapes (Cordts et al., 2016), COCO-Panoptic (Lin et al., 2014; Caesar et al., 2018), IDD (Varma et al., 2019), Mapillary Vistas (Neuhold et al., 2017), and SUN RGBD (Song et al., 2015).”
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation (2020, methodology): “For example, guardrail should not be discarded just because COCO, BDD, or IDD do not annotate it.”
EfficientPS: Efficient Panoptic Segmentation (2020, background): “Therefore, Chen et al (2018a) propose Dense Prediction Cells (DPC) and Valada et al (2019) propose Efficient Atrous Spatial Pyramid Pooling (eASPP) that yield better semantic segmentation performance than ASPP while being 10-times more efficient.”
MSeg: A Composite Dataset for Multi-Domain Semantic Segmentation (2022, background): “Kalluri et al. [25] mix pairs of datasets (Cityscapes + CamVid, Cityscapes + IDD, Cityscapes + SUN RGB-D) for semi-supervised learning.”
Decomposing NeRF for Editing via Feature Field Distillation (2022, methodology): “The model is trained on seven different datasets [37], including ADE20K [101], BDD [95], Cityscapes [16], COCO-Panoptic [41, 9], IDD [82], Mapillary Vistas [52], and SUN RGBD [74].”
Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild (2021, methodology): “…are a few wide-angle databases such as TorontoCity [51] and Wood-Scape [52], their scene variety and annotation density are far lower than pinhole benchmark datasets like Cityscapes [5], Mapillary Vistas [6], IDD [53] and BDD [54], which suppose rich ontologies for producing robust segmenters.”
Understanding Deep Learning Techniques for Image Segmentation (2019, neutral): “Natural Berkeley Segmentation Dataset [140] Scenes PASCAL VOC [54] Stanford Background Dataset [72] Microsoft COCO [122] MIT Scene parsing data(ADE20K) [222, 223] Semantic Boundaries Dataset [75] Microsoft Research Cambridge Object Recognition Image Database (MSRC) [188] Video Densely Annotated Vide”