Segmentation and detection challenge as part of AutoNUE 2019 which is being organized in conjunction with ICCV 2019.

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have immense societal and economic impact. The workshop adopts a broad view of what is entailed by driving in unconstrained environments. Some aspects such as changes in weather, time of day or imaging conditions are already being studied by the community, for instance, under the purview of domain adaptation.

Segmentation challenge on a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,000 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..

Autonomous driving has recently emerged as a keystone problem for computer vision and machine learning, with significant interest in both academia and industry. Besides being a rich source of research problems for visual perception, learning, mapping and planning, it is also poised to have immense societal and economic impact. The workshop adopts a broad view of what is entailed by driving in unconstrained environments. Some aspects such as changes in weather, time of day or imaging conditions are already being studied by the community, for instance, under the purview of domain adaptation.