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

Online Spatiotemporal Action Detection and Prediction via Causal Representations