  Spatiotemporal action recognition is the task of locating and classifying
actions in videos. Our project applies this task to analyzing video footage of
restaurant workers preparing food, for which potential applications include
automated checkout and inventory management. Such videos are quite different
from the standardized datasets that researchers are used to, as they involve
small objects, rapid actions, and notoriously unbalanced data classes. We
explore two approaches. The first approach involves the familiar object
detector You Only Look Once, and another applying a recently proposed analogue
for action recognition, You Only Watch Once. In the first, we design and
implement a novel, recurrent modification of YOLO using convolutional LSTMs and
explore the various subtleties in the training of such a network. In the
second, we study the ability of YOWOs three dimensional convolutions to capture
the spatiotemporal features of our unique dataset
