UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions


  • Marjaneh Safaei UCF
  • Pooyan Balouchian UCF
  • Hassan Foroosh UCF




Action recognition in still images poses a great challenge due to (i) fewer available training data, (ii) absence of temporal information. To address the first challenge, we introduce a dataset for STill image Action Recognition (STAR), containing over $1M$ images across 50 different human body-motion action categories. UCF-STAR is the largest dataset in the literature for action recognition in still images. The key characteristics of UCF-STAR include (1) focusing on human body-motion rather than relatively static human-object interaction categories, (2) collecting images from the wild to benefit from a varied set of action representations, (3) appending multiple human-annotated labels per image rather than just the action label, and (4) inclusion of rich, structured and multi-modal set of metadata for each image. This departs from existing datasets, which typically provide single annotation in a smaller number of images and categories, with no metadata. UCF-STAR exposes the intrinsic difficulty of action recognition through its realistic scene and action complexity. To benchmark and demonstrate the benefits of UCF-STAR as a large-scale dataset, and to show the role of “latent” motion information in recognizing human actions in still images, we present a novel approach relying on predicting temporal information, yielding higher accuracy on 5 widely-used datasets.




How to Cite

Safaei, M., Balouchian, P., & Foroosh, H. (2020). UCF-STAR: A Large Scale Still Image Dataset for Understanding Human Actions. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2677-2684. https://doi.org/10.1609/aaai.v34i03.5653



AAAI Technical Track: Humans and AI