Software & Data Downloads — MERL_Shopping_Dataset

MERL Shopping Dataset for demonstrating that our method significantly outperforms state-of-the-art action detection methods.

As part of this research, we collected a new dataset for training and testing action detection algorithms. Our MERL Shopping Dataset consists of 106 videos, each of which is a sequence about 2 minutes long. The videos are from a fixed overhead camera looking down at people shopping in a grocery store setting. Each video contains several instances of the following 5 actions: "Reach To Shelf" (reach hand into shelf), "Retract From Shelf " (retract hand from shelf), "Hand In Shelf" (extended period with hand in the shelf), "Inspect Product" (inspect product while holding it in hand), and "Inspect Shelf" (look at shelf while not touching or reaching for the shelf).

  •  Singh, B., Marks, T.K., Jones, M.J., Tuzel, C.O., Shao, M., "A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/​CVPR.2016.216, June 2016, pp. 1961-1970.
    BibTeX TR2016-080 PDF Data
    • @inproceedings{Singh2016jun,
    • author = {Singh, Bharat and Marks, Tim K. and Jones, Michael J. and Tuzel, C. Oncel and Shao, Ming},
    • title = {A Multi-Stream Bi-Directional Recurrent Neural Network for Fine-Grained Action Detection},
    • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    • year = 2016,
    • pages = {1961--1970},
    • month = jun,
    • doi = {10.1109/CVPR.2016.216},
    • url = {https://www.merl.com/publications/TR2016-080}
    • }

Access data at https://www.merl.com/pub/tmarks/MERL_Shopping_Dataset/.