Title: AMTnet: Action-Micro-Tube Regression by End-to-end Trainable Deep Architecture,
Abstract: Dominant approaches to action detection can only provide sub-optimal
solutions to the problem, as they rely on seeking frame-level detections, to
later compose them into "action tubes" in a post-processing step. With this
paper we radically depart from current practice, and take a first step towards
the design and implementation of a deep network architecture able to classify
and regress whole video subsets, so providing a truly optimal solution of the
action detection problem. In this work, in particular, we propose a novel deep
net framework able to regress and classify 3D region proposals spanning two
successive video frames, whose core is an evolution of classical region
proposal networks (RPNs). As such, our 3D-RPN net is able to effectively encode
the temporal aspect of actions by purely exploiting appearance, as opposed to
methods which heavily rely on expensive flow maps. The proposed model is
end-to-end trainable and can be jointly optimised for action localisation and
classification in a single step. At test time the network predicts
"micro-tubes" encompassing two successive frames, which are linked up into
complete action tubes via a new algorithm which exploits the temporal encoding
learned by the network and cuts computation time by 50%. Promising results on
the J-HMDB-21 and UCF-101 action detection datasets show that our model does
outperform the state-of-the-art when relying purely on appearance.