TICaM: A Time-of-flight In-car Cabin Monitoring Dataset

TICaM: A Time-of-flight In-car Cabin Monitoring Dataset
Jigyasa Singh Katrolia, Ahmed Elsherif, Hartmut Feld, Bruno Mirbach, Jason Raphael Rambach, Didier Stricker
British Machine Vision Conference (Hrsg.). Proceedings of the. British Machine Vision Conference (BMVC-2021) Online BMVA 2021 .

Abstract:

We present TICaM, a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. Our dataset goes beyond currently available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and annotations provided; all at the same time. We recorded an exhaustive list of actions performed while driving and provide for them multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection,instance and semantic segmentation as well as activity annotations for RGB frames. In addition to real recordings, we provide a synthetic dataset of in-car cabin images with the same multi-modality of images and annotations, contributing a unique and extremely beneficial combination of synthetic and real data for effectively training cabin monitoring systems and also evaluating domain adaptation approaches. We provide baseline evaluation for object detection, segmentation and transfer learning tasks on our dataset. The dataset is available here