IDD-X: A Multi-View Dataset for Ego-relative Important Object
  Localization and Explanation in Dense and Unstructured Traffic
Chirag Parikh Rohit Saluja C. V. Jawahar Ravi Kiran Sarvadevabhatla
Intelligent vehicle systems require a deep understanding of the interplay
between road conditions, surrounding entities, and the ego vehicle's driving
behavior for safe and efficient navigation. This is particularly critical in
developing countries where traffic situations are often dense and unstructured
with heterogeneous road occupants. Existing datasets, predominantly geared
towards structured and sparse traffic scenarios, fall short of capturing the
complexity of driving in such environments. To fill this gap, we present IDD-X,
a large-scale dual-view driving video dataset. With 697K bounding boxes, 9K
important object tracks, and 1-12 objects per video, IDD-X offers comprehensive
ego-relative annotations for multiple important road objects covering 10
categories and 19 explanation label categories. The dataset also incorporates
rearview information to provide a more complete representation of the driving
environment. We also introduce custom-designed deep networks aimed at multiple
important object localization and per-object explanation prediction. Overall,
our dataset and introduced prediction models form the foundation for studying
how road conditions and surrounding entities affect driving behavior in complex
traffic situations.