  Spatiotemporal action recognition deals with locating and classifying actions
in videos. Motivated by the latest state-of-the-art real-time object detector
You Only Watch Once (YOWO), we aim to modify its structure to increase action
detection precision and reduce computational time. Specifically, we propose
four novel approaches in attempts to improve YOWO and address the imbalanced
class issue in videos by modifying the loss function. We consider two
moderate-sized datasets to apply our modification of YOWO - the popular
Joint-annotated Human Motion Data Base (J-HMDB-21) and a private dataset of
restaurant video footage provided by a Carnegie Mellon University-based
startup, Agot.AI. The latter involves fast-moving actions with small objects as
well as unbalanced data classes, making the task of action localization more
challenging. We implement our proposed methods in the GitHub repository
https://github.com/stoneMo/YOWOv2.
