  Current state-of-the-art approaches for spatio-temporal action detection have
achieved impressive results but remain unsatisfactory for temporal extent
detection. The main reason comes from that, there are some ambiguous states
similar to the real actions which may be treated as target actions even by a
well-trained network. In this paper, we define these ambiguous samples as
"transitional states", and propose a Transition-Aware Context Network (TACNet)
to distinguish transitional states. The proposed TACNet includes two main
components, i.e., temporal context detector and transition-aware classifier.
The temporal context detector can extract long-term context information with
constant time complexity by constructing a recurrent network. The
transition-aware classifier can further distinguish transitional states by
classifying action and transitional states simultaneously. Therefore, the
proposed TACNet can substantially improve the performance of spatio-temporal
action detection. We extensively evaluate the proposed TACNet on UCF101-24 and
J-HMDB datasets. The experimental results demonstrate that TACNet obtains
competitive performance on JHMDB and significantly outperforms the
state-of-the-art methods on the untrimmed UCF101-24 in terms of both frame-mAP
and video-mAP.
