Qualitas Global, as a consortium partner of ARAI (Automotive Research Association of India), is actively involved in the creation of an Indian traffic dataset.

This dataset focuses on annotated database generation specifically for object detection in Indian road & traffic conditions. The aim is to provide annotated images with 2D bounding boxes that are suitable for training machine learning algorithms & developing Advanced Driver Assistance Systems (ADAS) functionalities.

The key feature of this technology is the ability to detect various objects & moments of the driver using image annotation, which is a well-known service for creating training data for computer vision applications.

While there are open-source annotated datasets available for US or European road environments like COCO & KITTI, there is a lack of datasets for Indian-specific road conditions. To address this gap, ARAI is collecting data under different environmental & road conditions & annotating objects on the captured camera images. The annotations are provided in open-domain formats such as Pascal, VOC & YOLO, utilizing 2D bounding boxes.

This initiative by Qualitas Global & ARAI benefits various industries, including vehicle manufacturers, research institutes working in AI/ML, startups developing algorithms for ADAS functionalities & enthusiasts focusing on developing machine learning algorithms for autonomous vehicle functionalities.