  Generating temporal action proposals remains a very challenging problem,
where the main issue lies in predicting precise temporal proposal boundaries
and reliable action confidence in long and untrimmed real-world videos. In this
paper, we propose an efficient and unified framework to generate temporal
action proposals named Dense Boundary Generator (DBG), which draws inspiration
from boundary-sensitive methods and implements boundary classification and
action completeness regression for densely distributed proposals. In
particular, the DBG consists of two modules: Temporal boundary classification
(TBC) and Action-aware completeness regression (ACR). The TBC aims to provide
two temporal boundary confidence maps by low-level two-stream features, while
the ACR is designed to generate an action completeness score map by high-level
action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to
encode RGB and optical flow information, which helps to capture discriminative
boundary and actionness features. Extensive experiments on popular benchmarks
ActivityNet-1.3 and THUMOS14 demonstrate the superiority of DBG over the
state-of-the-art proposal generator (e.g., MGG and BMN). Our code will be made
available upon publication.
