This code is for evaluating the Toyota Smarthome Untrimmed (TSU) dataset (Project page).
We introduce a new untrimmed daily-living dataset that features several real-world challenges: Toyota Smarthome Untrimmed (TSU). TSU contains a wide variety of activities performed in a spontaneous manner. Activities are collected in real-world settings, which results in non-optimal viewpoints. The dataset contains dense annotations including elementary, composite activities and activities involving interaction with objects. We provide an analysis of the real-world challenges featured by TSU dataset, highlighting the open issues for detection algorithms. We show that the current state-of-the-art methods fail to achieve satisfactory performance on the TSU dataset.
If you find this dataset useful for your research, please cite our paper:
@misc{dai2020toyota,
title={Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection},
author={Rui Dai and Srijan Das and Saurav Sharma and Luca Minciullo and Lorenzo Garattoni and Francois Bremond and Gianpiero Francesca},
year={2020},
eprint={2010.14982},
archivePrefix={arXiv},
primaryClass={cs.CV}
}