IndianRoad: A Video Dataset of Diverse Atomic Visual Elements in Dense and Unpredictable Environments

Xijun Wang, Pedro Sandoval-Segura, Tianrui Guan, Ruiqi Xian, Fuxiao Liu, Rohan Chandra, Boqing Gong, Dinesh Manocha

Keywords: Dataset, Vulnerable Road Users, Dense and Unpredictable Environment, Video Understanding, Behaviour Understanding

Abstract:

Most existing traffic video datasets including Waymo are structured, focusing predominantly on Western traffic, which hinders global applicability. Specifically, most Asian scenarios are far more complex, involving numerous objects with distinct motions and behaviors. Addressing this gap, we present a new dataset, IndianRoad, designed for evaluating perception methods with high representation of Vulnerable Road Users (VRUs: e.g. pedestrians, animals, motorbikes, and bicycles) in complex and unpredictable environments. IndianRoad is a manually annotated dataset encompassing 16 diverse actor categories (spanning animals, humans, vehicles, etc.) and 16 action types (complex and rare cases like cut-ins, zigzag movement, U-turn, etc.), which require high reasoning ability. IndianRoad densely annotates over 13 million bounding boxes (bboxes) actors with identification, and more than 1.6 million boxes are annotated with both actor identification and action/behavior details. The videos within IndianRoad are collected based on a broad spectrum of factors, such as weather conditions, the time of day, road scenarios, and traffic density. IndianRoad can benchmark video tasks like Tracking, Detection, Spatiotemporal Action Localization, Language-Visual Moment retrieval, and Multi-label Video Action Recognition. Given the critical importance of accurately identifying VRUs to prevent accidents and ensure road safety, in IndianRoad, vulnerable road users constitute 41.13% of instances, compared to 23.71% in Waymo. IndianRoad provides an invaluable resource for the development of more sensitive and accurate visual perception algorithms in the complex real world.
Our experiments show that existing methods suffer degradation in performance when evaluated on IndianRoad, highlighting its benefit for future video recognition research.

Primary Area: datasets and benchmarks

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Submission Number: 7719