Research Project: Predicting Future Actions
Emerging applications of artificial intelligence are bringing about important paradigm shifts in machine learning and computer vision. Machines need a comprehensive awareness of what takes place in complex environments, and to be able to use this understanding to make predictions about other machines' and humans' future behaviour.

In our first work we presented a method to predict an entire ‘action tube’ (a set of temporally linked bounding boxes) in a trimmed video just by observing a smaller subset of it. Predicting where an action is going to take place in the near future is essential to many computer vision based applications such as autonomous driving or surgical robotics. Importantly, it has to be done in realtime and in an online fashion. We propose a Tube Prediction network (TPnet) which jointly predicts the past, present and future bounding boxes along with their action classification scores. At test time TPnet is used in a (temporal) sliding window setting, and its predictions are put into a tube estimation framework to construct/predict the video long action tubes not only for the observed part of the video but also for the unobserved part. Additionally, the proposed action tube predictor helps in completing action tubes for unobserved segments of the video. We quantitatively demonstrate the latter ability, and the fact that TPnet improves state-of-the-art detection performance, on one of the standard action detection benchmarks - J-HMDB-21 dataset.
Relevant papers:
 Lab Member(s): Fabio Cuzzolin, Gurkirt Singh, Suman Saha