  In this thesis, we focus on video action understanding problems from an
online and real-time processing point of view. We start with the conversion of
the traditional offline spatiotemporal action detection pipeline into an online
spatiotemporal action tube detection system. An action tube is a set of
bounding connected over time, which bounds an action instance in space and
time. Next, we explore the future prediction capabilities of such detection
methods by extending an existing action tube into the future by regression.
Later, we seek to establish that online/causal representations can achieve
similar performance to that of offline three dimensional (3D) convolutional
neural networks (CNNs) on various tasks, including action recognition, temporal
action segmentation and early prediction.
