  Actionness was introduced to quantify the likelihood of containing a generic
action instance at a specific location. Accurate and efficient estimation of
actionness is important in video analysis and may benefit other relevant tasks
such as action recognition and action detection. This paper presents a new deep
architecture for actionness estimation, called hybrid fully convolutional
network (H-FCN), which is composed of appearance FCN (A-FCN) and motion FCN
(M-FCN). These two FCNs leverage the strong capacity of deep models to estimate
actionness maps from the perspectives of static appearance and dynamic motion,
respectively. In addition, the fully convolutional nature of H-FCN allows it to
efficiently process videos with arbitrary sizes. Experiments are conducted on
the challenging datasets of Stanford40, UCF Sports, and JHMDB to verify the
effectiveness of H-FCN on actionness estimation, which demonstrate that our
method achieves superior performance to previous ones. Moreover, we apply the
estimated actionness maps on action proposal generation and action detection.
Our actionness maps advance the current state-of-the-art performance of these
tasks substantially.
